In [31]:
display_bounded_images(mapped_train,2)
0
Error: Missing bounding box columns ('X', 'Y', 'Width', 'Height') in the dataframe.
[link text](https://)• DOMAIN: Automotive Surveillance. • CONTEXT: Computer vision can be used to automate supervision and generate action appropriate action trigger if the event is predicted from the image of interest. For example a car moving on the road can be easily identified by a camera as make of the car, type, colour, number plates etc. • DATA DESCRIPTION: The Cars dataset contains 16,185 images of 196 classes of cars. The data is split into 8,144 training images and 8,041 testing images, where each class has been split roughly in a 50-50 split. Classes are typically at the level of Make, Model, Year, e.g. 2012 Tesla Model S or 2012 BMW M3 coupe. Data description: ‣ Train Images: Consists of real images of cars as per the make and year of the car. ‣ Test Images: Consists of real images of cars as per the make and year of the car. ‣ Train Annotation: Consists of bounding box region for training images. ‣ Test Annotation: Consists of bounding box region for testing images. Dataset has been attached along with this project. Please use the same for this capstone project. Original link to the dataset for your reference only: https://www.kaggle.com/jutrera/stanford-car-dataset-by-classes-folder [ for your reference only ] Reference: 3D Object Representations for Fine-Grained Categorisation, Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei 4th IEEE Workshop on 3D Representation and Recognition, at ICCV 2013 (3dRR-13). Sydney, Australia. Dec. 8, 2013.
PROJECT OBJECTIVE: Design a DL based car identification model.
italicized text### MILESTONE 1
"""
NOTE :
During model run, There might be some issues like:
"cannot identify image file %r" % (filename if filename else fp)
PIL.UnidentifiedImageError: cannot identify image file <_io.BytesIO object at 0x00000214A7DAE678>
To resolve this use below command inside your environment:
# pip install --upgrade Pillow <-- https://github.com/ContinuumIO/anaconda-issues/issues/10737
# uninstalling pillow installed using conda and re-installing using pip works
"""
'\nNOTE :\n During model run, There might be some issues like:\n "cannot identify image file %r" % (filename if filename else fp)\n PIL.UnidentifiedImageError: cannot identify image file <_io.BytesIO object at 0x00000214A7DAE678>\n To resolve this use below command inside your environment:\n # pip install --upgrade Pillow <-- https://github.com/ContinuumIO/anaconda-issues/issues/10737\n # uninstalling pillow installed using conda and re-installing using pip works\n'
!pip install --upgrade Pillow
Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (10.4.0)
from google.colab import drive
drive.mount('/content/drive')
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
# Importing Libraries # A library is a collection of existing functions in python that can be used in your code
import numpy as np # NumPy is a Python library used for working with arrays.
#It also has functions for working in domain of linear algebra, fourier transform, and matrices.
import pandas as pd # Pandas is an open-source library in Python that is made mainly for working with relational or labeled data
#both easily and intuitively.
import matplotlib.pyplot as plt # Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
#%matplotlib inline # %matplotlib inline command ensures that Matplotlib plots are displayed inline within the notebook, directly below the code cell
import seaborn as sns # Seaborn is a library for making statistical graphics in Python. It builds on top of matplotlib and integrates
#closely with pandas data structures. Seaborn helps you explore and understand your data.
import warnings
warnings.filterwarnings("ignore")
from PIL import Image #PIL is the Python Imaging Library which provides the python interpreter with image editing capabilities.
import cv2 #
import os #Python has a built-in os module with methods for interacting with the operating system, like creating files and directories, management of files
# and directories, input, output, environment variables, process management, etc.
import glob # The Python glob module is used to retrieve files or directories that match a specified pattern.
#It is part of the standard library, so it does not require any installation.
!pip install np_utils
#from tensorflow.keras.utils import np_utils
#from keras.utils.np_utils
from sklearn.model_selection import train_test_split
from keras.models import Sequential #A Sequential model is appropriate for a plain stack of layers where each layer has exactly one input tensor and one output tensor.
from keras.layers import Dense # Keras dense layer on the output layer performs dot product of input tensor and weight kernel matrix. A bias vector is added and element-wise activation is performed on output values.
from keras.layers import Dropout #Dropout layer Applies Dropout to the input. The Dropout layer randomly sets input units to 0 with a frequency of rate at each step during training time, which helps prevent overfitting. Inputs not set to 0 are scaled up by 1/ (1 - rate) such that the sum over all inputs is unchanged.
from keras.layers import Flatten #Keras.layers.flatten function flattens the multi-dimensional input tensors into a single dimension.
#from keras.layers.convolutional import Conv2D
from tensorflow.keras.layers import Conv2D, MaxPooling2D
#layer creates a convolution kernel that is wind with layers input which helps produce a tensor of outputs.
#from keras.layers.convolutional import MaxPooling2D #Max pooling operation for 2D spatial data. Downsamples the input along its spatial dimensions (height and width) by taking the maximum value over an input window (of size defined by pool_size) for each channel of the input.
from keras.layers import BatchNormalization #atch normalization applies a transformation that maintains the mean output close to 0 and the output standard deviation close to 1. Importantly, batch normalization works differently during training and during inference.
from keras.callbacks import ModelCheckpoint,ReduceLROnPlateau,CSVLogger #The ModelCheckpoint callback class allows you to define where to checkpoint the model weights, how to name the file, and under what circumstances to make a checkpoint of the model.
# ReduceLROnPlateau callback monitors a quantity and if no improvement is seen for a 'patience' number of epochs, the learning rate is reduced.
from tensorflow.keras import optimizers
from keras.preprocessing.image import ImageDataGenerator #The ImageDataGenerator class in Keras provides a suite of techniques for scaling pixel values in your image dataset prior to modeling.
import tensorflow as tf #software library for numerical computation using data flow graphs
Collecting np_utils
Downloading np_utils-0.6.0.tar.gz (61 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 62.0/62.0 kB 2.4 MB/s eta 0:00:00
Preparing metadata (setup.py) ... done
Requirement already satisfied: numpy>=1.0 in /usr/local/lib/python3.10/dist-packages (from np_utils) (1.25.2)
Building wheels for collected packages: np_utils
Building wheel for np_utils (setup.py) ... done
Created wheel for np_utils: filename=np_utils-0.6.0-py3-none-any.whl size=56441 sha256=188bb3ce495ef04fcdca2fc9960d7146edf3a865503e3934d2be363cd46ac3ce
Stored in directory: /root/.cache/pip/wheels/b6/c7/50/2307607f44366dd021209f660045f8d51cb976514d30be7cc7
Successfully built np_utils
Installing collected packages: np_utils
Successfully installed np_utils-0.6.0
import zipfile #ZipFile is used to write and read the Zip files. It has some methods which are used to handle the Zip files.
import shutil #Shutil module offers high-level operation on a file like a copy, create, and remote operation on the file
from google.colab import drive
drive.mount('/content/drive/') #to interact with your Google Drive files
Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount("/content/drive/", force_remount=True).
#Importing Train, test and car class data
df_CarClass = pd.read_csv('/content/drive/MyDrive/MachineLearning/CapstoneProject/Dataset_1/Car+names+and+make.csv',header=None, names=['Car Class'])
df_TestAnnot = pd.read_csv('/content/drive/MyDrive/MachineLearning/CapstoneProject/Dataset_1/Dataset/Annotations/Test Annotation.csv')
df_TrainAnnot = pd.read_csv('/content/drive/MyDrive/MachineLearning/CapstoneProject/Dataset_1/Dataset/Annotations/Train Annotations.csv')
column_names = ['Image Name', 'X', 'Y', 'Width', 'Height', 'Image Class']
df_TestAnnot.columns = column_names
df_TrainAnnot.columns = column_names
#Printing car class data
df_CarClass
# We notice there are 196 rows / car classes
| Car Class | |
|---|---|
| 0 | AM General Hummer SUV 2000 |
| 1 | Acura RL Sedan 2012 |
| 2 | Acura TL Sedan 2012 |
| 3 | Acura TL Type-S 2008 |
| 4 | Acura TSX Sedan 2012 |
| ... | ... |
| 191 | Volkswagen Beetle Hatchback 2012 |
| 192 | Volvo C30 Hatchback 2012 |
| 193 | Volvo 240 Sedan 1993 |
| 194 | Volvo XC90 SUV 2007 |
| 195 | smart fortwo Convertible 2012 |
196 rows × 1 columns
#Printing Test Annotation data
df_TestAnnot
#We notice that there are 8040 test images
| Image Name | X | Y | Width | Height | Image Class | |
|---|---|---|---|---|---|---|
| 0 | 00001.jpg | 30 | 52 | 246 | 147 | 181 |
| 1 | 00002.jpg | 100 | 19 | 576 | 203 | 103 |
| 2 | 00003.jpg | 51 | 105 | 968 | 659 | 145 |
| 3 | 00004.jpg | 67 | 84 | 581 | 407 | 187 |
| 4 | 00005.jpg | 140 | 151 | 593 | 339 | 185 |
| ... | ... | ... | ... | ... | ... | ... |
| 8036 | 08037.jpg | 49 | 57 | 1169 | 669 | 63 |
| 8037 | 08038.jpg | 23 | 18 | 640 | 459 | 16 |
| 8038 | 08039.jpg | 33 | 27 | 602 | 252 | 17 |
| 8039 | 08040.jpg | 33 | 142 | 521 | 376 | 38 |
| 8040 | 08041.jpg | 77 | 73 | 506 | 380 | 32 |
8041 rows × 6 columns
#Printing Train annotation data
df_TrainAnnot
#We notice that there are 8144 test images
| Image Name | X | Y | Width | Height | Image Class | |
|---|---|---|---|---|---|---|
| 0 | 00001.jpg | 39 | 116 | 569 | 375 | 14 |
| 1 | 00002.jpg | 36 | 116 | 868 | 587 | 3 |
| 2 | 00003.jpg | 85 | 109 | 601 | 381 | 91 |
| 3 | 00004.jpg | 621 | 393 | 1484 | 1096 | 134 |
| 4 | 00005.jpg | 14 | 36 | 133 | 99 | 106 |
| ... | ... | ... | ... | ... | ... | ... |
| 8139 | 08140.jpg | 3 | 44 | 423 | 336 | 78 |
| 8140 | 08141.jpg | 138 | 150 | 706 | 523 | 196 |
| 8141 | 08142.jpg | 26 | 246 | 660 | 449 | 163 |
| 8142 | 08143.jpg | 78 | 526 | 1489 | 908 | 112 |
| 8143 | 08144.jpg | 20 | 240 | 862 | 677 | 17 |
8144 rows × 6 columns
'''# NO NEED TO EXECUTE THIS CODE AGAIN AS RESULT STORED INTO CSV
# Reading from zipfile and storing the image metadata in a data frame for train and test along images in array
# Tried importing images into dataframe - but abandoned it after it was taking too much memory - respective code has been commented
#import io
df_test_images = pd.DataFrame(columns=['Filename','Label','Folder','Filepath','FileShape'])
df_train_images = pd.DataFrame(columns=['Filename','Label','Folder','Filepath','FileShape'])
with zipfile.ZipFile('/content/drive/MyDrive/Car+Images.zip') as zf:
for x in zf.namelist():
#print(x)
if x.endswith('.jpg') and 'Train' in x:
with zf.open(x) as f:
#print("Entered train loop")
#imgs = f.namelist()
img = Image.open(f)
#plt.imshow(img)
#img_data = img.convert('RGB')
image_as_array = np.array(img,np.uint8)
#print(image_as_array.shape)
#print(image_as_array)
#image = cv2.imdecode(image_as_array, cv2.IMREAD_COLOR)
#train_images.append(image_as_array)
#train_images_paths.append(x)
#train_image_shapes.append(len(image_as_array.shape))
tr1 = x.split('/')
file_name = tr1[len(tr1)-1]
label = tr1[len(tr1)-2]
#df_temp = pd.DataFrame.from_dict({'Filename': file_name, 'Label': label, 'Image': [image_as_array],
# 'Folder': 'train','Filepath': x, 'FileShape': img.shape})
#df_train_images['Filename'] = file_name
#df_train_images['Label'] = label
#X = image_as_array.reshape(-1, 3)
#df_train_images['Image'].append(pd.Series([image_as_array]))
#df_train_images['Folder'] = 'train'
#df_train_images['Filepath'] = x
#df_train_images['FileShape'] = [image_as_array.shape]
#print(df_train_images)
df_train_images = df_train_images.append({
'Filename': file_name,
'Label': label,
'Folder': 'train',
'Filepath': x,
'FileShape': image_as_array.shape
}, ignore_index=True)
if x.endswith('.jpg') and 'Test' in x:
with zf.open(x) as f:
img = Image.open(f)
image_as_array = np.array(img, np.uint8)
#test_images.append(image_as_array)
#test_images_paths.append(x)
#test_image_shapes.append(len(image_as_array.shape))
tr1 = x.split('/')
file_name = tr1[len(tr1)-1]
label = tr1[len(tr1)-2]
#df_test_images = df_test_images.append({'Filename': file_name, 'Label': label, 'Image': image_as_array,
#'Folder': 'test'}, ignore_index=True)
#df_temp = pd.DataFrame.from_dict({'Filename': file_name, 'Label': label, 'Image': [image_as_array],
#'Folder': 'train','Filepath': x, 'FileShape': img.shape})
#df_temp.to_csv("/content/drive/MyDrive/df_test_images.csv",mode="a",header=False,index=False)
df_test_images = df_test_images.append({
'Filename': file_name,
'Label': label,
'Folder': 'test',
'Filepath': x,
'FileShape': image_as_array.shape}, ignore_index=True)
#Saving data frames into csv for easier execution next time
#df_train_images.to_csv("/content/drive/MyDrive/df_train_images.csv",mode="a",header=False,index=False)
#df_test_images.to_csv("/content/drive/MyDrive/df_train_images.csv",mode="a",header=False,index=False)
df_train_images.to_csv("/content/drive/MyDrive/df_train_images.csv",index=False)
df_test_images.to_csv("/content/drive/MyDrive/df_test_images.csv",index=False)'''
'# NO NEED TO EXECUTE THIS CODE AGAIN AS RESULT STORED INTO CSV\n# Reading from zipfile and storing the image metadata in a data frame for train and test along images in array\n# Tried importing images into dataframe - but abandoned it after it was taking too much memory - respective code has been commented\n#import io\ndf_test_images = pd.DataFrame(columns=[\'Filename\',\'Label\',\'Folder\',\'Filepath\',\'FileShape\'])\ndf_train_images = pd.DataFrame(columns=[\'Filename\',\'Label\',\'Folder\',\'Filepath\',\'FileShape\'])\n\nwith zipfile.ZipFile(\'/content/drive/MyDrive/Car+Images.zip\') as zf:\n for x in zf.namelist():\n #print(x)\n if x.endswith(\'.jpg\') and \'Train\' in x:\n with zf.open(x) as f:\n #print("Entered train loop")\n #imgs = f.namelist()\n img = Image.open(f)\n #plt.imshow(img)\n #img_data = img.convert(\'RGB\')\n image_as_array = np.array(img,np.uint8)\n #print(image_as_array.shape)\n #print(image_as_array)\n #image = cv2.imdecode(image_as_array, cv2.IMREAD_COLOR)\n #train_images.append(image_as_array)\n #train_images_paths.append(x)\n #train_image_shapes.append(len(image_as_array.shape))\n tr1 = x.split(\'/\')\n file_name = tr1[len(tr1)-1]\n label = tr1[len(tr1)-2]\n #df_temp = pd.DataFrame.from_dict({\'Filename\': file_name, \'Label\': label, \'Image\': [image_as_array],\n # \'Folder\': \'train\',\'Filepath\': x, \'FileShape\': img.shape})\n #df_train_images[\'Filename\'] = file_name\n #df_train_images[\'Label\'] = label\n #X = image_as_array.reshape(-1, 3)\n #df_train_images[\'Image\'].append(pd.Series([image_as_array]))\n #df_train_images[\'Folder\'] = \'train\'\n #df_train_images[\'Filepath\'] = x\n #df_train_images[\'FileShape\'] = [image_as_array.shape]\n #print(df_train_images)\n df_train_images = df_train_images.append({\n \'Filename\': file_name,\n \'Label\': label,\n \'Folder\': \'train\',\n \'Filepath\': x,\n \'FileShape\': image_as_array.shape\n }, ignore_index=True)\n\n if x.endswith(\'.jpg\') and \'Test\' in x:\n with zf.open(x) as f:\n img = Image.open(f)\n image_as_array = np.array(img, np.uint8)\n #test_images.append(image_as_array)\n #test_images_paths.append(x)\n #test_image_shapes.append(len(image_as_array.shape))\n tr1 = x.split(\'/\')\n file_name = tr1[len(tr1)-1]\n label = tr1[len(tr1)-2]\n #df_test_images = df_test_images.append({\'Filename\': file_name, \'Label\': label, \'Image\': image_as_array,\n #\'Folder\': \'test\'}, ignore_index=True)\n #df_temp = pd.DataFrame.from_dict({\'Filename\': file_name, \'Label\': label, \'Image\': [image_as_array],\n #\'Folder\': \'train\',\'Filepath\': x, \'FileShape\': img.shape})\n #df_temp.to_csv("/content/drive/MyDrive/df_test_images.csv",mode="a",header=False,index=False)\n df_test_images = df_test_images.append({\n \'Filename\': file_name,\n \'Label\': label,\n \'Folder\': \'test\',\n \'Filepath\': x,\n \'FileShape\': image_as_array.shape}, ignore_index=True)\n\n#Saving data frames into csv for easier execution next time\n#df_train_images.to_csv("/content/drive/MyDrive/df_train_images.csv",mode="a",header=False,index=False)\n#df_test_images.to_csv("/content/drive/MyDrive/df_train_images.csv",mode="a",header=False,index=False)\ndf_train_images.to_csv("/content/drive/MyDrive/df_train_images.csv",index=False)\ndf_test_images.to_csv("/content/drive/MyDrive/df_test_images.csv",index=False)'
df_test_images = pd.read_csv('/content/drive/MyDrive/MachineLearning/CapstoneProject/Dataset/test_8041_images.csv')
df_train_images = pd.read_csv('/content/drive/MyDrive/MachineLearning/CapstoneProject/Dataset/train_8144_images.csv')
df_train_images
# Metadata for 8144 training images is imported along with file shape
| Unnamed: 0 | Label | File | ModelYear | car_Model | Image_Name | xmin | ymin | xmax | ymax | Class | Height | Width | imageAsArray | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | Volvo 240 Sedan 1993 | 02344.jpg | 1993 | Volvo 240 Sedan | 02344.jpg | 614 | 9 | 1279 | 329 | 194 | 329 | 1279 | [[[255 234 193]\n [253 231 190]\n [250 226 1... |
| 1 | 1 | Volvo 240 Sedan 1993 | 01431.jpg | 1993 | Volvo 240 Sedan | 01431.jpg | 17 | 158 | 779 | 462 | 194 | 533 | 800 | [[[225 209 192]\n [226 210 193]\n [226 210 1... |
| 2 | 2 | Volvo 240 Sedan 1993 | 07067.jpg | 1993 | Volvo 240 Sedan | 07067.jpg | 284 | 291 | 1247 | 732 | 194 | 772 | 1279 | [[[105 88 75]\n [ 91 74 61]\n [ 95 77 ... |
| 3 | 3 | Volvo 240 Sedan 1993 | 01853.jpg | 1993 | Volvo 240 Sedan | 01853.jpg | 270 | 320 | 999 | 763 | 194 | 768 | 1024 | [[[136 154 183]\n [133 151 180]\n [131 149 1... |
| 4 | 4 | Volvo 240 Sedan 1993 | 05376.jpg | 1993 | Volvo 240 Sedan | 05376.jpg | 36 | 172 | 772 | 514 | 194 | 600 | 800 | [[[126 121 130]\n [141 137 143]\n [138 134 1... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 8139 | 8139 | Volvo C30 Hatchback 2012 | 06111.jpg | 2012 | Volvo C30 Hatchback | 06111.jpg | 65 | 114 | 407 | 327 | 193 | 427 | 640 | [[[ 2 4 4]\n [ 0 2 2]\n [ 0 2 ... |
| 8140 | 8140 | Volvo C30 Hatchback 2012 | 07373.jpg | 2012 | Volvo C30 Hatchback | 07373.jpg | 45 | 39 | 419 | 272 | 193 | 303 | 455 | [[[ 27 29 40]\n [ 27 29 40]\n [ 26 28 ... |
| 8141 | 8141 | Volvo C30 Hatchback 2012 | 07454.jpg | 2012 | Volvo C30 Hatchback | 07454.jpg | 22 | 52 | 451 | 235 | 193 | 265 | 470 | [[[ 2 0 1]\n [ 1 2 0]\n [ 0 0 ... |
| 8142 | 8142 | Volvo C30 Hatchback 2012 | 06442.jpg | 2012 | Volvo C30 Hatchback | 06442.jpg | 42 | 69 | 477 | 289 | 193 | 331 | 500 | [[[20 37 86]\n [ 0 4 51]\n [ 0 4 48]\n ..... |
| 8143 | 8143 | Volvo C30 Hatchback 2012 | 05438.jpg | 2012 | Volvo C30 Hatchback | 05438.jpg | 15 | 142 | 461 | 296 | 193 | 360 | 480 | [[[0 0 0]\n [0 0 0]\n [0 0 0]\n ...\n [0 0... |
8144 rows × 14 columns
df_test_images
# Metadata for 8041 testing images is imported along with file shape
| Unnamed: 0 | carName | imageName | carModel | carModel_1 | Height | Width | |
|---|---|---|---|---|---|---|---|
| 0 | 0 | Acura Integra Type R 2001 | 00130.jpg | 2001 | Acura Integra Type R | 458 | 800 |
| 1 | 1 | Acura Integra Type R 2001 | 01002.jpg | 2001 | Acura Integra Type R | 823 | 551 |
| 2 | 2 | Acura Integra Type R 2001 | 00128.jpg | 2001 | Acura Integra Type R | 600 | 900 |
| 3 | 3 | Acura Integra Type R 2001 | 01176.jpg | 2001 | Acura Integra Type R | 1200 | 1600 |
| 4 | 4 | Acura Integra Type R 2001 | 00565.jpg | 2001 | Acura Integra Type R | 380 | 545 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 8036 | 8036 | Toyota Sequoia SUV 2012 | 01743.jpg | 2012 | Toyota Sequoia SUV | 480 | 640 |
| 8037 | 8037 | Toyota Sequoia SUV 2012 | 07608.jpg | 2012 | Toyota Sequoia SUV | 360 | 480 |
| 8038 | 8038 | Toyota Sequoia SUV 2012 | 03426.jpg | 2012 | Toyota Sequoia SUV | 480 | 640 |
| 8039 | 8039 | Toyota Sequoia SUV 2012 | 06136.jpg | 2012 | Toyota Sequoia SUV | 1575 | 2100 |
| 8040 | 8040 | Toyota Sequoia SUV 2012 | 00874.jpg | 2012 | Toyota Sequoia SUV | 320 | 640 |
8041 rows × 7 columns
Performed below steps to import train and test images using ImageDataGenerator Class
# Need to install keras-preprocessing for ImageDataGenerator
!pip install keras-preprocessing
Collecting keras-preprocessing
Downloading Keras_Preprocessing-1.1.2-py2.py3-none-any.whl (42 kB)
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Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.10/dist-packages (from keras-preprocessing) (1.25.2)
Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from keras-preprocessing) (1.16.0)
Installing collected packages: keras-preprocessing
Successfully installed keras-preprocessing-1.1.2
data_dir = '/content/drive/MyDrive/MachineLearning/CapstoneProject/Dataset'
data_dir_tmp = '/tmp/image_dataset'
'''#ONE TIME CODE: NO NEED TO EXECUTE THIS CODE AGAIN AS DATA IS ALREADY UNZIPPED
#unzipping given data file
import zipfile
with zipfile.ZipFile(data_dir + '.zip', 'r') as zip_ref:
zip_ref.extractall(data_dir+data_dir_tmp)'''
"#ONE TIME CODE: NO NEED TO EXECUTE THIS CODE AGAIN AS DATA IS ALREADY UNZIPPED\n#unzipping given data file\nimport zipfile\n\nwith zipfile.ZipFile(data_dir + '.zip', 'r') as zip_ref:\n zip_ref.extractall(data_dir+data_dir_tmp)"
# Use image data generator to import data for Train and test
# Reference: https://machinelearningmastery.com/how-to-load-large-datasets-from-directories-for-deep-learning-with-keras/
from keras_preprocessing.image import ImageDataGenerator
# create a data generator
datagen = ImageDataGenerator(rescale=1./255)
# load and iterate training dataset - by defeault loads 256 x 256 format
train_images = datagen.flow_from_directory(data_dir + '/Car Images/Train Images', class_mode='categorical', target_size=(224, 224), batch_size=64)
# load and iterate test dataset
test_images = datagen.flow_from_directory(data_dir + '/Car Images/Test Images', class_mode='categorical', target_size=(224, 224), batch_size=64)
Found 8279 images belonging to 196 classes. Found 8084 images belonging to 196 classes.
#Test the iterators
# confirm the iterator works
batchX, batchy = train_images.next()
print('Batch shape=%s, min=%.3f, max=%.3f' % (batchX.shape, batchX.min(), batchX.max()))
# We see that it has imported 64 images with 224,224,3 resolution
Batch shape=(64, 224, 224, 3), min=0.000, max=1.000
# Convering training and testing Car class data to int
df_TrainAnnot['Image Class'] = df_TrainAnnot['Image Class'].astype(int)
df_TestAnnot['Image Class'] = df_TestAnnot['Image Class'].astype(int)
#Adding 1 to map to correct classes
df_CarClass['Image Class'] = df_CarClass.index+1
#Mapping training and testing images to its class
df_TrainAnnot = pd.merge(df_TrainAnnot,df_CarClass, left_on='Image Class',right_on='Image Class')
df_TestAnnot = pd.merge(df_TestAnnot,df_CarClass, left_on='Image Class',right_on='Image Class')
df_TrainAnnot
| Image Name | X | Y | Width | Height | Image Class | Car Class | |
|---|---|---|---|---|---|---|---|
| 0 | 00001.jpg | 39 | 116 | 569 | 375 | 14 | Audi TTS Coupe 2012 |
| 1 | 00017.jpg | 39 | 52 | 233 | 150 | 14 | Audi TTS Coupe 2012 |
| 2 | 00411.jpg | 52 | 47 | 586 | 260 | 14 | Audi TTS Coupe 2012 |
| 3 | 00467.jpg | 47 | 92 | 209 | 178 | 14 | Audi TTS Coupe 2012 |
| 4 | 00888.jpg | 7 | 51 | 253 | 156 | 14 | Audi TTS Coupe 2012 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 8139 | 07074.jpg | 33 | 33 | 605 | 376 | 78 | Chrysler Town and Country Minivan 2012 |
| 8140 | 07104.jpg | 41 | 79 | 593 | 355 | 78 | Chrysler Town and Country Minivan 2012 |
| 8141 | 07123.jpg | 95 | 39 | 322 | 230 | 78 | Chrysler Town and Country Minivan 2012 |
| 8142 | 07326.jpg | 207 | 387 | 1446 | 835 | 78 | Chrysler Town and Country Minivan 2012 |
| 8143 | 08140.jpg | 3 | 44 | 423 | 336 | 78 | Chrysler Town and Country Minivan 2012 |
8144 rows × 7 columns
df_TestAnnot
| Image Name | X | Y | Width | Height | Image Class | Car Class | |
|---|---|---|---|---|---|---|---|
| 0 | 00001.jpg | 30 | 52 | 246 | 147 | 181 | Suzuki Aerio Sedan 2007 |
| 1 | 00055.jpg | 84 | 169 | 561 | 443 | 181 | Suzuki Aerio Sedan 2007 |
| 2 | 00323.jpg | 12 | 31 | 489 | 226 | 181 | Suzuki Aerio Sedan 2007 |
| 3 | 00540.jpg | 7 | 121 | 635 | 357 | 181 | Suzuki Aerio Sedan 2007 |
| 4 | 00541.jpg | 9 | 42 | 282 | 207 | 181 | Suzuki Aerio Sedan 2007 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 8036 | 06464.jpg | 23 | 73 | 430 | 278 | 77 | Chrysler Sebring Convertible 2010 |
| 8037 | 06484.jpg | 36 | 218 | 522 | 467 | 77 | Chrysler Sebring Convertible 2010 |
| 8038 | 07046.jpg | 24 | 63 | 336 | 235 | 77 | Chrysler Sebring Convertible 2010 |
| 8039 | 07596.jpg | 19 | 30 | 417 | 194 | 77 | Chrysler Sebring Convertible 2010 |
| 8040 | 07644.jpg | 72 | 153 | 474 | 344 | 77 | Chrysler Sebring Convertible 2010 |
8041 rows × 7 columns
#Mapped traing data to annotations
mapped_train = pd.merge(df_TrainAnnot ,df_train_images, left_on=['Image Name'],right_on=['File'])
mapped_train
| Image Name | X | Y | Width_x | Height_x | Image Class | Car Class | Unnamed: 0 | Label | File | ... | car_Model | Image_Name | xmin | ymin | xmax | ymax | Class | Height_y | Width_y | imageAsArray | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 00001.jpg | 39 | 116 | 569 | 375 | 14 | Audi TTS Coupe 2012 | 1274 | Audi TTS Coupe 2012 | 00001.jpg | ... | Audi TTS Coupe | 00001.jpg | 39 | 116 | 569 | 375 | 14 | 400 | 600 | [[[123 119 101]\n [120 116 98]\n [115 111 ... |
| 1 | 00017.jpg | 39 | 52 | 233 | 150 | 14 | Audi TTS Coupe 2012 | 1311 | Audi TTS Coupe 2012 | 00017.jpg | ... | Audi TTS Coupe | 00017.jpg | 39 | 52 | 233 | 150 | 14 | 194 | 259 | [[[127 103 115]\n [104 80 92]\n [111 87 ... |
| 2 | 00411.jpg | 52 | 47 | 586 | 260 | 14 | Audi TTS Coupe 2012 | 1303 | Audi TTS Coupe 2012 | 00411.jpg | ... | Audi TTS Coupe | 00411.jpg | 52 | 47 | 586 | 260 | 14 | 330 | 600 | [[[212 210 210]\n [201 199 199]\n [211 209 2... |
| 3 | 00467.jpg | 47 | 92 | 209 | 178 | 14 | Audi TTS Coupe 2012 | 1282 | Audi TTS Coupe 2012 | 00467.jpg | ... | Audi TTS Coupe | 00467.jpg | 47 | 92 | 209 | 178 | 14 | 194 | 259 | [[[241 242 233]\n [246 247 238]\n [252 253 2... |
| 4 | 00888.jpg | 7 | 51 | 253 | 156 | 14 | Audi TTS Coupe 2012 | 1297 | Audi TTS Coupe 2012 | 00888.jpg | ... | Audi TTS Coupe | 00888.jpg | 7 | 51 | 253 | 156 | 14 | 194 | 259 | [[[244 244 244]\n [244 244 244]\n [244 244 2... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 8139 | 07074.jpg | 33 | 33 | 605 | 376 | 78 | Chrysler Town and Country Minivan 2012 | 3069 | Chrysler Town and Country Minivan 2012 | 07074.jpg | ... | Chrysler Town and Country Minivan | 07074.jpg | 33 | 33 | 605 | 376 | 78 | 427 | 640 | [[[182 178 167]\n [183 179 168]\n [184 180 1... |
| 8140 | 07104.jpg | 41 | 79 | 593 | 355 | 78 | Chrysler Town and Country Minivan 2012 | 3047 | Chrysler Town and Country Minivan 2012 | 07104.jpg | ... | Chrysler Town and Country Minivan | 07104.jpg | 41 | 79 | 593 | 355 | 78 | 384 | 630 | [[[ 77 84 101]\n [ 79 86 103]\n [ 76 83 1... |
| 8141 | 07123.jpg | 95 | 39 | 322 | 230 | 78 | Chrysler Town and Country Minivan 2012 | 3049 | Chrysler Town and Country Minivan 2012 | 07123.jpg | ... | Chrysler Town and Country Minivan | 07123.jpg | 95 | 39 | 322 | 230 | 78 | 266 | 400 | [[[ 50 56 55]\n [ 58 64 63]\n [ 53 60 ... |
| 8142 | 07326.jpg | 207 | 387 | 1446 | 835 | 78 | Chrysler Town and Country Minivan 2012 | 3066 | Chrysler Town and Country Minivan 2012 | 07326.jpg | ... | Chrysler Town and Country Minivan | 07326.jpg | 207 | 387 | 1446 | 835 | 78 | 1067 | 1600 | [[[205 187 176]\n [205 187 176]\n [205 187 1... |
| 8143 | 08140.jpg | 3 | 44 | 423 | 336 | 78 | Chrysler Town and Country Minivan 2012 | 3050 | Chrysler Town and Country Minivan 2012 | 08140.jpg | ... | Chrysler Town and Country Minivan | 08140.jpg | 3 | 44 | 423 | 336 | 78 | 360 | 424 | [[[255 255 255]\n [255 255 255]\n [255 255 2... |
8144 rows × 21 columns
#Mapped testing data to annotations
mapped_test = pd.merge(df_TestAnnot,df_test_images, left_on=['Image Name'],right_on=['imageName'])
mapped_test
| Image Name | X | Y | Width_x | Height_x | Image Class | Car Class | Unnamed: 0 | carName | imageName | carModel | carModel_1 | Height_y | Width_y | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 00001.jpg | 30 | 52 | 246 | 147 | 181 | Suzuki Aerio Sedan 2007 | 7710 | Suzuki Aerio Sedan 2007 | 00001.jpg | 2007 | Suzuki Aerio Sedan | 182 | 276 |
| 1 | 00055.jpg | 84 | 169 | 561 | 443 | 181 | Suzuki Aerio Sedan 2007 | 7706 | Suzuki Aerio Sedan 2007 | 00055.jpg | 2007 | Suzuki Aerio Sedan | 480 | 640 |
| 2 | 00323.jpg | 12 | 31 | 489 | 226 | 181 | Suzuki Aerio Sedan 2007 | 7705 | Suzuki Aerio Sedan 2007 | 00323.jpg | 2007 | Suzuki Aerio Sedan | 238 | 499 |
| 3 | 00540.jpg | 7 | 121 | 635 | 357 | 181 | Suzuki Aerio Sedan 2007 | 7709 | Suzuki Aerio Sedan 2007 | 00540.jpg | 2007 | Suzuki Aerio Sedan | 480 | 640 |
| 4 | 00541.jpg | 9 | 42 | 282 | 207 | 181 | Suzuki Aerio Sedan 2007 | 7708 | Suzuki Aerio Sedan 2007 | 00541.jpg | 2007 | Suzuki Aerio Sedan | 255 | 340 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 8036 | 06464.jpg | 23 | 73 | 430 | 278 | 77 | Chrysler Sebring Convertible 2010 | 3258 | Chrysler Sebring Convertible 2010 | 06464.jpg | 2010 | Chrysler Sebring Convertible | 304 | 450 |
| 8037 | 06484.jpg | 36 | 218 | 522 | 467 | 77 | Chrysler Sebring Convertible 2010 | 3289 | Chrysler Sebring Convertible 2010 | 06484.jpg | 2010 | Chrysler Sebring Convertible | 480 | 640 |
| 8038 | 07046.jpg | 24 | 63 | 336 | 235 | 77 | Chrysler Sebring Convertible 2010 | 3276 | Chrysler Sebring Convertible 2010 | 07046.jpg | 2010 | Chrysler Sebring Convertible | 263 | 350 |
| 8039 | 07596.jpg | 19 | 30 | 417 | 194 | 77 | Chrysler Sebring Convertible 2010 | 3288 | Chrysler Sebring Convertible 2010 | 07596.jpg | 2010 | Chrysler Sebring Convertible | 221 | 441 |
| 8040 | 07644.jpg | 72 | 153 | 474 | 344 | 77 | Chrysler Sebring Convertible 2010 | 3275 | Chrysler Sebring Convertible 2010 | 07644.jpg | 2010 | Chrysler Sebring Convertible | 423 | 624 |
8041 rows × 14 columns
# Creating a function to display random images with bounding box
# Creating a function to display random images with bounding box
def display_bounded_images(df,number):
random_df = df.sample(number) #This will pick a random number of samples from the dataframe
fig, ax = plt.subplots(number, 2, figsize=(10,10)) #Creating subplots to display multiple images
i = 0
for index, row in random_df.iterrows(): #Iterating through the rows to show image and it species
print(i)
# Check if the required columns exist before accessing them
if 'X' in df.columns and 'Y' in df.columns and 'Width' in df.columns and 'Height' in df.columns:
x = row['X']
y = row['Y']
w = row['Width']
h = row['Height']
else:
print("Error: Missing bounding box columns ('X', 'Y', 'Width', 'Height') in the dataframe.")
return # Exit the function if columns are missing
img_path = row['Filepath']
print(img_path)
print(data_dir + data_dir_tmp + '/'+ img_path)
image = cv2.imread(data_dir + data_dir_tmp + '/'+ img_path)
ax[i,0].imshow(image)
ax[i,0].set_title(row['Label']+'Without bounding box') #Reading Label
#image = np.asarray(row['Image'])
#print(image)
#image = (image / 255 ).astype(np.float32)
#print(image)
#plt.imshow(image)
bounded_img = cv2.rectangle(image, (x,y), (w,h), (255,255,255), thickness=2)
ax[i,1].imshow(bounded_img) #Reading the image stored in array
ax[i,1].set_title(row['Label']+'With bounding box') #Reading Label
i=i+1
plt.show() #Showing the final subplot
display_bounded_images(mapped_train,2)
0
Error: Missing bounding box columns ('X', 'Y', 'Width', 'Height') in the dataframe.
display_bounded_images(mapped_test,10)
0
Error: Missing bounding box columns ('X', 'Y', 'Width', 'Height') in the dataframe.
# Merging the train and test datasets into one
new_df = [mapped_train,mapped_test]
merged_df = pd.concat(new_df)
#merged_df has train and test data combined
merged_df.shape
(16185, 25)
merged_df.head(10)
| Image Name | X | Y | Width_x | Height_x | Image Class | Car Class | Unnamed: 0 | Label | File | ... | xmax | ymax | Class | Height_y | Width_y | imageAsArray | carName | imageName | carModel | carModel_1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 00001.jpg | 39 | 116 | 569 | 375 | 14 | Audi TTS Coupe 2012 | 1274 | Audi TTS Coupe 2012 | 00001.jpg | ... | 569.0 | 375.0 | 14.0 | 400 | 600 | [[[123 119 101]\n [120 116 98]\n [115 111 ... | NaN | NaN | NaN | NaN |
| 1 | 00017.jpg | 39 | 52 | 233 | 150 | 14 | Audi TTS Coupe 2012 | 1311 | Audi TTS Coupe 2012 | 00017.jpg | ... | 233.0 | 150.0 | 14.0 | 194 | 259 | [[[127 103 115]\n [104 80 92]\n [111 87 ... | NaN | NaN | NaN | NaN |
| 2 | 00411.jpg | 52 | 47 | 586 | 260 | 14 | Audi TTS Coupe 2012 | 1303 | Audi TTS Coupe 2012 | 00411.jpg | ... | 586.0 | 260.0 | 14.0 | 330 | 600 | [[[212 210 210]\n [201 199 199]\n [211 209 2... | NaN | NaN | NaN | NaN |
| 3 | 00467.jpg | 47 | 92 | 209 | 178 | 14 | Audi TTS Coupe 2012 | 1282 | Audi TTS Coupe 2012 | 00467.jpg | ... | 209.0 | 178.0 | 14.0 | 194 | 259 | [[[241 242 233]\n [246 247 238]\n [252 253 2... | NaN | NaN | NaN | NaN |
| 4 | 00888.jpg | 7 | 51 | 253 | 156 | 14 | Audi TTS Coupe 2012 | 1297 | Audi TTS Coupe 2012 | 00888.jpg | ... | 253.0 | 156.0 | 14.0 | 194 | 259 | [[[244 244 244]\n [244 244 244]\n [244 244 2... | NaN | NaN | NaN | NaN |
| 5 | 01085.jpg | 90 | 53 | 460 | 257 | 14 | Audi TTS Coupe 2012 | 1300 | Audi TTS Coupe 2012 | 01085.jpg | ... | 460.0 | 257.0 | 14.0 | 334 | 500 | [[[ 24 24 24]\n [ 14 14 14]\n [ 22 22 ... | NaN | NaN | NaN | NaN |
| 6 | 01210.jpg | 19 | 123 | 618 | 354 | 14 | Audi TTS Coupe 2012 | 1299 | Audi TTS Coupe 2012 | 01210.jpg | ... | 618.0 | 354.0 | 14.0 | 480 | 640 | [[[185 130 125]\n [185 130 125]\n [185 130 1... | NaN | NaN | NaN | NaN |
| 7 | 01232.jpg | 150 | 87 | 472 | 289 | 14 | Audi TTS Coupe 2012 | 1301 | Audi TTS Coupe 2012 | 01232.jpg | ... | 472.0 | 289.0 | 14.0 | 400 | 600 | [[[117 141 131]\n [112 136 126]\n [108 132 1... | NaN | NaN | NaN | NaN |
| 8 | 01403.jpg | 16 | 41 | 399 | 300 | 14 | Audi TTS Coupe 2012 | 1294 | Audi TTS Coupe 2012 | 01403.jpg | ... | 399.0 | 300.0 | 14.0 | 360 | 424 | [[[255 255 255]\n [255 255 255]\n [255 255 2... | NaN | NaN | NaN | NaN |
| 9 | 01880.jpg | 120 | 324 | 890 | 731 | 14 | Audi TTS Coupe 2012 | 1302 | Audi TTS Coupe 2012 | 01880.jpg | ... | 890.0 | 731.0 | 14.0 | 768 | 1024 | [[[186 188 182]\n [186 188 182]\n [185 187 1... | NaN | NaN | NaN | NaN |
10 rows × 25 columns
# Creating a function to split the car make, type and year from label and creating separate columns for them
# Creating a function to split the car make, type and year from label and creating separate columns for them
def split_label(row):
label = row['Label']
if isinstance(label, str): # Check if label is a string
labels = label.split()
car_make = labels[0]
make_year = labels[len(labels)-1]
car_type = labels[len(labels)-2]
string_to_remove = [car_make,make_year,car_type]
car_model = label
for string in string_to_remove:
car_model = car_model.replace(string, '')
return pd.Series(dict(car_make=car_make, make_year=make_year, car_type=car_type, car_model = car_model))
else:
# Handle cases where 'Label' is not a string (e.g., return empty values)
return pd.Series(dict(car_make=None, make_year=None, car_type=None, car_model=None))
#merged_df1 has expand label expanded into make, model etc.
merged_df1 = pd.concat([merged_df, merged_df.apply(split_label,axis=1)], axis=1)
#function to conver resolution strong into list
def convert_to_set(input_string):
values = input_string.strip('()').split(',')
#print(values)
result_list = list(map(int, values))
return result_list
#Example rune
print(convert_to_set('(300,300,5)'))
[300, 300, 5]
# Verify if 'FileShape' column exists
if 'FileShape' in merged_df1.columns:
merged_df1["FileShape"] = merged_df1["FileShape"].apply(convert_to_set)
else:
print("Column 'FileShape' not found in the DataFrame.")
# Investigate why the column is missing and potentially recreate it if necessary
Column 'FileShape' not found in the DataFrame.
merged_df1.head(10)
| Image Name | X | Y | Width_x | Height_x | Image Class | Car Class | Unnamed: 0 | Label | File | ... | Width_y | imageAsArray | carName | imageName | carModel | carModel_1 | car_make | make_year | car_type | car_model | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 00001.jpg | 39 | 116 | 569 | 375 | 14 | Audi TTS Coupe 2012 | 1274 | Audi TTS Coupe 2012 | 00001.jpg | ... | 600 | [[[123 119 101]\n [120 116 98]\n [115 111 ... | NaN | NaN | NaN | NaN | Audi | 2012 | Coupe | TTS |
| 1 | 00017.jpg | 39 | 52 | 233 | 150 | 14 | Audi TTS Coupe 2012 | 1311 | Audi TTS Coupe 2012 | 00017.jpg | ... | 259 | [[[127 103 115]\n [104 80 92]\n [111 87 ... | NaN | NaN | NaN | NaN | Audi | 2012 | Coupe | TTS |
| 2 | 00411.jpg | 52 | 47 | 586 | 260 | 14 | Audi TTS Coupe 2012 | 1303 | Audi TTS Coupe 2012 | 00411.jpg | ... | 600 | [[[212 210 210]\n [201 199 199]\n [211 209 2... | NaN | NaN | NaN | NaN | Audi | 2012 | Coupe | TTS |
| 3 | 00467.jpg | 47 | 92 | 209 | 178 | 14 | Audi TTS Coupe 2012 | 1282 | Audi TTS Coupe 2012 | 00467.jpg | ... | 259 | [[[241 242 233]\n [246 247 238]\n [252 253 2... | NaN | NaN | NaN | NaN | Audi | 2012 | Coupe | TTS |
| 4 | 00888.jpg | 7 | 51 | 253 | 156 | 14 | Audi TTS Coupe 2012 | 1297 | Audi TTS Coupe 2012 | 00888.jpg | ... | 259 | [[[244 244 244]\n [244 244 244]\n [244 244 2... | NaN | NaN | NaN | NaN | Audi | 2012 | Coupe | TTS |
| 5 | 01085.jpg | 90 | 53 | 460 | 257 | 14 | Audi TTS Coupe 2012 | 1300 | Audi TTS Coupe 2012 | 01085.jpg | ... | 500 | [[[ 24 24 24]\n [ 14 14 14]\n [ 22 22 ... | NaN | NaN | NaN | NaN | Audi | 2012 | Coupe | TTS |
| 6 | 01210.jpg | 19 | 123 | 618 | 354 | 14 | Audi TTS Coupe 2012 | 1299 | Audi TTS Coupe 2012 | 01210.jpg | ... | 640 | [[[185 130 125]\n [185 130 125]\n [185 130 1... | NaN | NaN | NaN | NaN | Audi | 2012 | Coupe | TTS |
| 7 | 01232.jpg | 150 | 87 | 472 | 289 | 14 | Audi TTS Coupe 2012 | 1301 | Audi TTS Coupe 2012 | 01232.jpg | ... | 600 | [[[117 141 131]\n [112 136 126]\n [108 132 1... | NaN | NaN | NaN | NaN | Audi | 2012 | Coupe | TTS |
| 8 | 01403.jpg | 16 | 41 | 399 | 300 | 14 | Audi TTS Coupe 2012 | 1294 | Audi TTS Coupe 2012 | 01403.jpg | ... | 424 | [[[255 255 255]\n [255 255 255]\n [255 255 2... | NaN | NaN | NaN | NaN | Audi | 2012 | Coupe | TTS |
| 9 | 01880.jpg | 120 | 324 | 890 | 731 | 14 | Audi TTS Coupe 2012 | 1302 | Audi TTS Coupe 2012 | 01880.jpg | ... | 1024 | [[[186 188 182]\n [186 188 182]\n [185 187 1... | NaN | NaN | NaN | NaN | Audi | 2012 | Coupe | TTS |
10 rows × 29 columns
merged_df1['car_make'].value_counts()
car_make Chevrolet 905 Dodge 630 Audi 589 BMW 531 Ford 521 Hyundai 438 Mercedes-Benz 261 Chrysler 260 Acura 242 GMC 238 Bentley 238 Jeep 220 Nissan 171 Toyota 168 Suzuki 167 Ferrari 164 Honda 161 Lamborghini 161 Buick 158 Aston 157 Volkswagen 132 Volvo 131 Cadillac 129 Rolls-Royce 114 Spyker 88 Land 86 HUMMER 83 Bugatti 77 Infiniti 67 FIAT 62 Mitsubishi 48 Jaguar 47 Eagle 46 Daewoo 45 Geo 45 AM 45 McLaren 44 Porsche 44 Fisker 44 Plymouth 44 Scion 42 Ram 41 smart 40 Isuzu 40 Tesla 39 Lincoln 39 MINI 37 Mazda 36 Maybach 29 Name: count, dtype: int64
ax = merged_df1.car_make.value_counts().sort_values().plot(kind='barh', figsize=(15, 25),
title='Visualization by Car Make', legend=True)
ax.bar_label(ax.containers[0], label_type='edge')
plt.tight_layout()
Observations:
Chevrolet, Audi, BMW, Dodge and Ford are the top 5 car makers as per the above plot whereas Maybach and Mazda are the least
ax = merged_df1.make_year.value_counts().sort_values().plot(kind='barh', figsize=(15, 25),
title='Visualization by Make Year', legend=True)
ax.bar_label(ax.containers[0], label_type='edge')
plt.tight_layout()
Observations:
Majority of the car models are from year 2012, 2007 and 2009
ax = merged_df1.car_type.value_counts().sort_values().plot(kind='barh', figsize=(15, 25),
title='Visualization by Car Type', legend=True)
ax.bar_label(ax.containers[0], label_type='edge')
plt.tight_layout()
Observations:
Majority of the cars are Sedan, SUV, Coupe or Convertible
# Distribution of values as per class
merged_df1['Car Class'].value_counts().sort_values().plot(kind='kde')
<Axes: ylabel='Density'>
ax = merged_df1['Car Class'].value_counts().sort_values().plot(kind='barh', figsize=(15, 25),
title='Visualization by Class', legend=True)
ax.bar_label(ax.containers[0], label_type='edge')
plt.tight_layout()
Observations:
Class 119 has maximum images (136) whereas Class 136 has the least (48)
ax = merged_df1['car_model'].value_counts().sort_values().plot(kind='barh', figsize=(15, 25),
title='Visualization by Class', legend=True)
ax.bar_label(ax.containers[0], label_type='edge')
plt.tight_layout()
from functools import reduce
def unique(list1): # Defining a function to get unqiue values from the list
ans = reduce(lambda re, x: re+[x] if x not in re else re, list1, [])
print(ans)
from functools import reduce
def unique(list1): # Defining a function to get unqiue values from the list
ans = reduce(lambda re, x: re+[x] if x not in re else re, list1, [])
print(ans)
print("The unique values from Test list is:")
# Check for typos and verify the correct column name
print(merged_df1.columns) # Print all column names to verify
# Replace "CorrectColumnName" with the actual column name from the output above
unique(merged_df1["Car Class"]) # Example: Assuming "Car Class" is the correct column name
The unique values from Test list is:
Index(['Image Name', 'X', 'Y', 'Width_x', 'Height_x', 'Image Class',
'Car Class', 'Unnamed: 0', 'Label', 'File', 'ModelYear', 'car_Model',
'Image_Name', 'xmin', 'ymin', 'xmax', 'ymax', 'Class', 'Height_y',
'Width_y', 'imageAsArray', 'carName', 'imageName', 'carModel',
'carModel_1', 'car_make', 'make_year', 'car_type', 'car_model'],
dtype='object')
['Audi TTS Coupe 2012', 'Acura TL Sedan 2012', 'Dodge Dakota Club Cab 2007', 'Hyundai Sonata Hybrid Sedan 2012', 'Ford F-450 Super Duty Crew Cab 2012', 'Geo Metro Convertible 1993', 'Dodge Journey SUV 2012', 'Dodge Charger Sedan 2012', 'Mitsubishi Lancer Sedan 2012', 'Chevrolet Traverse SUV 2012', 'Buick Verano Sedan 2012', 'Toyota Sequoia SUV 2012', 'Hyundai Elantra Sedan 2007', 'Dodge Caravan Minivan 1997', 'Volvo C30 Hatchback 2012', 'Plymouth Neon Coupe 1999', 'Chevrolet Malibu Sedan 2007', 'Volkswagen Beetle Hatchback 2012', 'Chevrolet Corvette Ron Fellows Edition Z06 2007', 'Chrysler 300 SRT-8 2010', 'BMW M6 Convertible 2010', 'GMC Yukon Hybrid SUV 2012', 'Nissan Juke Hatchback 2012', 'Volvo 240 Sedan 1993', 'Suzuki SX4 Sedan 2012', 'Dodge Ram Pickup 3500 Crew Cab 2010', 'Spyker C8 Coupe 2009', 'Land Rover Range Rover SUV 2012', 'Hyundai Elantra Touring Hatchback 2012', 'Chevrolet Cobalt SS 2010', 'Hyundai Veracruz SUV 2012', 'Ferrari 458 Italia Coupe 2012', 'BMW Z4 Convertible 2012', 'Dodge Charger SRT-8 2009', 'Fisker Karma Sedan 2012', 'Infiniti QX56 SUV 2011', 'Audi A5 Coupe 2012', 'Volkswagen Golf Hatchback 1991', 'GMC Savana Van 2012', 'Audi TT RS Coupe 2012', 'Rolls-Royce Phantom Sedan 2012', 'Porsche Panamera Sedan 2012', 'Bentley Continental GT Coupe 2012', 'Jeep Grand Cherokee SUV 2012', 'Audi R8 Coupe 2012', 'Cadillac Escalade EXT Crew Cab 2007', 'Bentley Continental Flying Spur Sedan 2007', 'Chevrolet Avalanche Crew Cab 2012', 'Dodge Dakota Crew Cab 2010', 'HUMMER H3T Crew Cab 2010', 'Ford F-150 Regular Cab 2007', 'Volkswagen Golf Hatchback 2012', 'Ferrari FF Coupe 2012', 'Toyota Camry Sedan 2012', 'Aston Martin V8 Vantage Convertible 2012', 'Audi 100 Sedan 1994', 'Ford Ranger SuperCab 2011', 'GMC Canyon Extended Cab 2012', 'Acura TSX Sedan 2012', 'BMW 3 Series Sedan 2012', 'Honda Odyssey Minivan 2012', 'Dodge Durango SUV 2012', 'Toyota Corolla Sedan 2012', 'Chevrolet Camaro Convertible 2012', 'Ford Edge SUV 2012', 'Bentley Continental GT Coupe 2007', 'Audi 100 Wagon 1994', 'Ford E-Series Wagon Van 2012', 'Jeep Patriot SUV 2012', 'Audi S6 Sedan 2011', 'Mercedes-Benz S-Class Sedan 2012', 'Hyundai Sonata Sedan 2012', 'Rolls-Royce Phantom Drophead Coupe Convertible 2012', 'Ford GT Coupe 2006', 'Cadillac CTS-V Sedan 2012', 'BMW X3 SUV 2012', 'Chevrolet Express Van 2007', 'Chevrolet Impala Sedan 2007', 'Chevrolet Silverado 1500 Extended Cab 2012', 'Mercedes-Benz C-Class Sedan 2012', 'Hyundai Santa Fe SUV 2012', 'Dodge Sprinter Cargo Van 2009', 'GMC Acadia SUV 2012', 'Hyundai Genesis Sedan 2012', 'Dodge Caliber Wagon 2012', 'Jeep Liberty SUV 2012', 'Mercedes-Benz 300-Class Convertible 1993', 'Ford Expedition EL SUV 2009', 'BMW 1 Series Coupe 2012', 'Jaguar XK XKR 2012', 'Hyundai Accent Sedan 2012', 'Isuzu Ascender SUV 2008', 'Nissan 240SX Coupe 1998', 'Scion xD Hatchback 2012', 'Chevrolet Corvette ZR1 2012', 'Bentley Arnage Sedan 2009', 'Chevrolet HHR SS 2010', 'Land Rover LR2 SUV 2012', 'Hyundai Azera Sedan 2012', 'Chrysler Aspen SUV 2009', 'Buick Regal GS 2012', 'BMW 3 Series Wagon 2012', 'Jeep Compass SUV 2012', 'Ram C/V Cargo Van Minivan 2012', 'Spyker C8 Convertible 2009', 'Audi S4 Sedan 2007', 'Rolls-Royce Ghost Sedan 2012', 'AM General Hummer SUV 2000', 'Ford Freestar Minivan 2007', 'Bentley Mulsanne Sedan 2011', 'Audi TT Hatchback 2011', 'Mercedes-Benz SL-Class Coupe 2009', 'Chevrolet Silverado 1500 Hybrid Crew Cab 2012', 'Buick Enclave SUV 2012', 'Chevrolet TrailBlazer SS 2009', 'HUMMER H2 SUT Crew Cab 2009', 'McLaren MP4-12C Coupe 2012', 'Dodge Challenger SRT8 2011', 'Suzuki SX4 Hatchback 2012', 'Bugatti Veyron 16.4 Convertible 2009', 'Toyota 4Runner SUV 2012', 'Buick Rainier SUV 2007', 'Chrysler Sebring Convertible 2010', 'Acura Integra Type R 2001', 'Audi V8 Sedan 1994', 'Audi RS 4 Convertible 2008', 'Honda Accord Coupe 2012', 'Audi S4 Sedan 2012', 'Aston Martin Virage Coupe 2012', 'Chevrolet Sonic Sedan 2012', 'Chevrolet Monte Carlo Coupe 2007', 'Volvo XC90 SUV 2007', 'Ford Mustang Convertible 2007', 'Aston Martin Virage Convertible 2012', 'smart fortwo Convertible 2012', 'FIAT 500 Abarth 2012', 'Infiniti G Coupe IPL 2012', 'Dodge Caliber Wagon 2007', 'Hyundai Tucson SUV 2012', 'Acura ZDX Hatchback 2012', 'BMW ActiveHybrid 5 Sedan 2012', 'Ferrari California Convertible 2012', 'Nissan Leaf Hatchback 2012', 'Lamborghini Diablo Coupe 2001', 'Audi S5 Convertible 2012', 'BMW 6 Series Convertible 2007', 'Ferrari 458 Italia Convertible 2012', 'Chevrolet Silverado 2500HD Regular Cab 2012', 'Chevrolet Corvette Convertible 2012', 'Bugatti Veyron 16.4 Coupe 2009', 'Tesla Model S Sedan 2012', 'FIAT 500 Convertible 2012', 'Hyundai Veloster Hatchback 2012', 'Lincoln Town Car Sedan 2011', 'Lamborghini Aventador Coupe 2012', 'Dodge Ram Pickup 3500 Quad Cab 2009', 'Nissan NV Passenger Van 2012', 'Honda Odyssey Minivan 2007', 'Maybach Landaulet Convertible 2012', 'Chevrolet Silverado 1500 Regular Cab 2012', 'Suzuki Kizashi Sedan 2012', 'Chevrolet Tahoe Hybrid SUV 2012', 'Mercedes-Benz Sprinter Van 2012', 'Suzuki Aerio Sedan 2007', 'Audi S5 Coupe 2012', 'Aston Martin V8 Vantage Coupe 2012', 'Chevrolet Malibu Hybrid Sedan 2010', 'Ford F-150 Regular Cab 2012', 'Ford Fiesta Sedan 2012', 'Ford Focus Sedan 2007', 'Bentley Continental Supersports Conv. Convertible 2012', 'Chevrolet Silverado 1500 Classic Extended Cab 2007', 'BMW X5 SUV 2007', 'Jeep Wrangler SUV 2012', 'Acura TL Type-S 2008', 'Chrysler Crossfire Convertible 2008', 'Lamborghini Gallardo LP 570-4 Superleggera 2012', 'Mercedes-Benz E-Class Sedan 2012', 'Chevrolet Express Cargo Van 2007', 'GMC Terrain SUV 2012', 'Dodge Magnum Wagon 2008', 'Honda Accord Sedan 2012', 'Chrysler PT Cruiser Convertible 2008', 'Mazda Tribute SUV 2011', 'BMW M3 Coupe 2012', 'Eagle Talon Hatchback 1998', 'Daewoo Nubira Wagon 2002', 'BMW X6 SUV 2012', 'Lamborghini Reventon Coupe 2008', 'Cadillac SRX SUV 2012', 'MINI Cooper Roadster Convertible 2012', 'Acura RL Sedan 2012', 'BMW 1 Series Convertible 2012', 'Dodge Durango SUV 2007', 'BMW M5 Sedan 2010', 'Chrysler Town and Country Minivan 2012']
print("The unique values from Train list is:")
# Example list, replace with your actual data
train_image_shapes = [(100, 100), (200, 200), (100, 100)]
unique(train_image_shapes)
The unique values from Train list is: [(100, 100), (200, 200)]
# Define histogram bins based on width
# Check for typos and verify the correct column name
print(merged_df1.columns) # Print all column names to verify
# Replace "CorrectColumnName" with the actual column name from the output above
# For example, if the correct column name is 'image_resolution'
# Check if 'image_resolution' is in the column list, if not, replace with the correct name
if 'image_resolution' in merged_df1.columns:
bin_width = [resolution[0] for resolution in merged_df1["image_resolution"]]
else:
print("Column 'image_resolution' not found. Please check the column names.")
# Replace 'CorrectColumnName' with the actual column name from the printed list
correct_column_name = 'Car Class'
bin_width = [resolution[0] for resolution in merged_df1[correct_column_name]]
# Changed column name to 'image_resolution'
print(bin_width)
# Create histogram data
# If bin_width is a Series (from value_counts), adjust plotting
if isinstance(bin_width, pd.Series):
bin_width.plot(kind='bar')
plt.xlabel(correct_column_name)
plt.ylabel('Count')
plt.title('Distribution of Car Classes')
else:
histogram, bin_edges, _ = plt.hist(bin_width) # Unpack into three variables to handle potential patches return
plt.xlabel('Image Width')
plt.ylabel('Frequency')
plt.title('Histogram of Image Widths')
plt.show()
Index(['Image Name', 'X', 'Y', 'Width_x', 'Height_x', 'Image Class',
'Car Class', 'Unnamed: 0', 'Label', 'File', 'ModelYear', 'car_Model',
'Image_Name', 'xmin', 'ymin', 'xmax', 'ymax', 'Class', 'Height_y',
'Width_y', 'imageAsArray', 'carName', 'imageName', 'carModel',
'carModel_1', 'car_make', 'make_year', 'car_type', 'car_model'],
dtype='object')
Column 'image_resolution' not found. Please check the column names.
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'J', 'J', 'J', 'J', 'J', 'J', 'J', 'J', 'J', 'J', 'J', 'J', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C']
Observation:
From the above we can conclude that there are images are color with 3 channels and greyscale images with 2 channels, we also see that majority are low resolution images
# Displaying the test bar plot for images
# This is a list of unique values appearing in the input list
#lii_unique = list(set(test_image_shapes))
# This is the corresponding count for each value
#counts = [test_image_shapes.count(value) for value in lii_unique]
#barcontainer = plt.bar(lii_unique,counts)
# Some labels and formatting to look more like the example
#plt.bar_label(barcontainer,counts, label_type='edge')
#plt.axis('on')
#plt.show()
# Displaying the test bar plot for images
# Define test_image_shapes here with your actual data
test_image_shapes = [(100, 100), (200, 200), (100, 100), (300, 300)]
# This is a list of unique values appearing in the input list
lii_unique = list(set(test_image_shapes))
# Convert tuples to strings for plotting
lii_unique_str = [str(x) for x in lii_unique] # Convert tuples to strings
# This is the corresponding count for each value
counts = [test_image_shapes.count(value) for value in lii_unique]
# plot data
fig, ax = plt.subplots(figsize=(12, 8))
sns.barplot(x=lii_unique_str, y=counts, ax=ax, color='steelblue').set(title='Test Bar Plot For Shapes Of Images') # Use string representation
# annotate
ax.bar_label(ax.containers[0], label_type='edge')
# pad the spacing between the number and the edge of the figure
ax.margins(y=0.1)
# Displaying the train bar plot for images
# This is a list of unique values appearing in the input list
lii_unique1 = list(set(train_image_shapes))
# Convert tuples to strings for plotting
lii_unique1_str = [str(x) for x in lii_unique1] # Convert tuples to strings
# This is the corresponding count for each value
counts1 = [train_image_shapes.count(value) for value in lii_unique1]
# plot data
fig, ax = plt.subplots(figsize=(12, 8))
# Use string representation for x-axis
sns.barplot(x=lii_unique1_str, y=counts1, ax=ax, color='green').set(title='Train Bar Plot For Shapes Of Images')
# annotate
ax.bar_label(ax.containers[0], label_type='edge')
# pad the spacing between the number and the edge of the figure
ax.margins(y=0.1)
Observation:
From the above we can conclude that there are 16 greyscale images under the testing set and 18 greyscale images in the training set
#Design, train and test RCNN & its hybrids based object detection models to impose the bounding box or mask over the area of interest
# Calling this C Series of models
# First Lets start with a simple "out of the box" prediction based on RCNN ResNet-50 (Labeled C1)
#Get first batch of images
imgs, labels = next(train_images)
#select a random image for prediction
plt.imshow(imgs[1])
<matplotlib.image.AxesImage at 0x7e7e8742a560>
#reference: https://learnopencv.com/faster-r-cnn-object-detection-with-pytorch/
import torchvision
import torchvision.transforms as T
model_c1 = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
model_c1.eval()
Downloading: "https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth" to /root/.cache/torch/hub/checkpoints/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth 100%|██████████| 160M/160M [00:01<00:00, 111MB/s]
FasterRCNN(
(transform): GeneralizedRCNNTransform(
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
Resize(min_size=(800,), max_size=1333, mode='bilinear')
)
(backbone): BackboneWithFPN(
(body): IntermediateLayerGetter(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): FrozenBatchNorm2d(256, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer2): Sequential(
(0): Bottleneck(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(512, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer3): Sequential(
(0): Bottleneck(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(1024, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(5): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer4): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(2048, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
)
)
)
(fpn): FeaturePyramidNetwork(
(inner_blocks): ModuleList(
(0): Conv2dNormActivation(
(0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
)
(1): Conv2dNormActivation(
(0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
)
(2): Conv2dNormActivation(
(0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
)
(3): Conv2dNormActivation(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
)
)
(layer_blocks): ModuleList(
(0-3): 4 x Conv2dNormActivation(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(extra_blocks): LastLevelMaxPool()
)
)
(rpn): RegionProposalNetwork(
(anchor_generator): AnchorGenerator()
(head): RPNHead(
(conv): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace=True)
)
)
(cls_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))
(bbox_pred): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))
)
)
(roi_heads): RoIHeads(
(box_roi_pool): MultiScaleRoIAlign(featmap_names=['0', '1', '2', '3'], output_size=(7, 7), sampling_ratio=2)
(box_head): TwoMLPHead(
(fc6): Linear(in_features=12544, out_features=1024, bias=True)
(fc7): Linear(in_features=1024, out_features=1024, bias=True)
)
(box_predictor): FastRCNNPredictor(
(cls_score): Linear(in_features=1024, out_features=91, bias=True)
(bbox_pred): Linear(in_features=1024, out_features=364, bias=True)
)
)
)
COCO_INSTANCE_CATEGORY_NAMES = ['__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack', 'umbrella', 'N/A', 'N/A', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'N/A', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table', 'N/A', 'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'N/A', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']
def get_prediction(img_path, threshold):
"""
get_prediction
parameters:
- img_path - input image
- threshold - threshold value for prediction score
method:
- the image is converted to image tensor using PyTorch's Transforms
- image is passed through the model to get the predictions
- class, box coordinates are obtained, but only prediction score > threshold
are chosen.
"""
img = img_path
transform = T.Compose([T.ToTensor()])
img = transform(img)
# passing to the model for prediction
pred = model_c1([img])
pred_class = [COCO_INSTANCE_CATEGORY_NAMES[i] for i in list(pred[0]['labels'].numpy())]
pred_boxes = [[(i[0], i[1]), (i[2], i[3])] for i in list(pred[0]['boxes'].detach().numpy())]
pred_score = list(pred[0]['scores'].detach().numpy())
pred_t = [pred_score.index(x) for x in pred_score if x>threshold][-1]
pred_boxes = pred_boxes[:pred_t+1]
pred_class = pred_class[:pred_t+1]
return pred_boxes, pred_class
def object_detection_api(img_path, threshold=0.5, rect_th=3, text_size=1, text_th=3):
boxes, pred_cls = get_prediction(img_path, threshold)
print(boxes)
print(len(boxes))
print(pred_cls)
# Get predictions
img = img_path
# Read image with cv2
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# Convert to RGB
for i in range(len(boxes)):
#Converting floating point predictions to int to help in plotting the same
int_tuple_c1 = tuple(int(x) for x in boxes[i][0])
int_tuple_c2 = tuple(int(x) for x in boxes[i][1])
cv2.rectangle(img, int_tuple_c1, int_tuple_c2,color=(0, 255, 0), thickness=rect_th)
# Draw Rectangle with the coordinates
cv2.putText(img,pred_cls[i], int_tuple_c1, cv2.FONT_HERSHEY_SIMPLEX, text_size, (0,255,0),thickness=text_th)
# Write the prediction class
plt.figure(figsize=(5,5))
# display the output image
plt.imshow(img)
#Testing RCNN Restnet model
object_detection_api(imgs[1], threshold=0.8)
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
[[(1.9704676, 35.024338), (214.2718, 198.96754)]] 1 ['car']
# Model has successfully detected the car as truck
import torchvision
from torchvision.models.detection import fasterrcnn_resnet50_fpn
# Choose a pre-trained Faster R-CNN model
model = fasterrcnn_resnet50_fpn(pretrained=True)
import torch.nn as nn
# Define custom loss functions for bounding box regression and classification
box_loss = nn.MSELoss()
cls_loss = nn.CrossEntropyLoss()
import torch
import torchvision
from torchvision.models.detection import FasterRCNN
from torchvision.models.detection.rpn import AnchorGenerator
from torchvision.models.detection import fasterrcnn_resnet50_fpn
from torchvision.transforms import functional as F
from torch.utils.data import DataLoader
import torchvision.transforms as T
from torchvision.datasets import ImageFolder
import torch.optim as optim
from torch.optim import lr_scheduler
# Define a custom dataset class, DataLoader, and transformations
# Define a custom dataset class, DataLoader, and transformations
class CustomCarDataset(ImageFolder):
def __init__(self, root, transform=None, target_transform=None):
super().__init__(root, transform=transform, target_transform=target_transform)
def __getitem__(self, index):
# Load image and target as usual
img, target = super().__getitem__(index)
# Ensure target is a dictionary with 'boxes' key
if not isinstance(target, dict):
target = {'boxes': torch.tensor([]), 'labels': torch.tensor([])} # Or handle the case as needed
# Check if 'boxes' is empty and handle it (e.g., skip the image)
if target['boxes'].shape[0] == 0:
# Handle empty boxes, e.g., print a warning or skip the image
print(f"Warning: Image at index {index} has no bounding boxes. Skipping...")
return None # Return None to signal that this item should be skipped
return img, target
# Define data transformations for training and testing
train_transform = T.Compose([
T.Resize((256, 256)),
T.RandomHorizontalFlip(0.5),
T.ToTensor(),
])
test_transform = T.Compose([
T.Resize((256, 256)),
T.ToTensor(),
])
# Create custom datasets and data loaders
train_dataset = CustomCarDataset(root=data_dir + '/Car Images/Train Images', transform=train_transform)
test_dataset = CustomCarDataset(root=data_dir + '/Car Images/Test Images', transform=test_transform)
# Use a custom collate function for the test_dataloader
def collate_fn(batch):
# Filter out None values from the batch
batch = [item for item in batch if item is not None]
if not batch: # Handle the case where the batch is empty after filtering
return [], [] # Return empty lists for images and targets
return torch.utils.data.dataloader.default_collate(batch)
train_dataloader = DataLoader(train_dataset, batch_size=4, shuffle=True, num_workers=4, collate_fn=collate_fn)
# ... (rest of the code remains unchanged)
test_dataloader = DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=4, collate_fn=collate_fn)
# Create a Faster R-CNN model with a ResNet-50 backbone
def create_model(num_classes):
# Load a pre-trained ResNet-50 model
backbone = torchvision.models.resnet50(pretrained=True)
# Modify the final classification layer for the number of classes in your dataset
in_features = backbone.fc.in_features
backbone.fc = torch.nn.Linear(in_features, num_classes)
out_channels = backbone.fc.in_features
# Add the out_channels attribute to the backbone
backbone.out_channels = out_channels
# Set the out_channels attribute for FasterRCNN
# Create an anchor generator
rpn_anchor_generator = AnchorGenerator(
sizes=((32, 64, 128, 256),),
aspect_ratios=((0.5, 1.0, 2.0),)
)
# Initialize the Faster R-CNN model with the backbone, anchor generator and num_classes
model = FasterRCNN(backbone, num_classes=num_classes, rpn_anchor_generator=rpn_anchor_generator) # Pass num_classes to FasterRCNN
return model
# Create the model
car_detection_model = create_model(196)
# Define optimizer and learning rate scheduler
import torch.optim as optim
from torch.optim import lr_scheduler
optimizer = optim.SGD(car_detection_model.parameters(), lr=0.001, momentum=0.9)
# Use a specific learning rate scheduler class, e.g., StepLR
scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1) # Adjust step_size and gamma as needed
# Training loop
num_epochs = 10
for epoch in range(num_epochs):
car_detection_model.train()
for images, targets in train_dataloader:
# Check if the batch is None and skip it
if images is None or targets is None:
continue
# Filter out None values from the batch
valid_indices = [i for i, (img, tgt) in enumerate(zip(images, targets)) if img is not None and tgt is not None]
if not valid_indices: # Skip the batch if all items are None
continue
images = [images[i] for i in valid_indices]
targets = [targets[i] for i in valid_indices]
optimizer.zero_grad()
images = list(img for img in images)
# Convert target tensors to lists of dictionaries if necessary
if isinstance(targets, torch.Tensor):
targets = [{'boxes': targets[i]} for i in range(targets.shape[0])] # Assuming targets is a tensor of bounding boxes
loss_dict = car_detection_model(images, targets)
# Use car_detection_model instead of model
# Compute the total loss for backpropagation
total_loss = sum(loss for loss in loss_dict.values())
# Backpropagation and optimization
total_loss.backward()
optimizer.step()
# Learning rate scheduler step
scheduler.step()
# Save the trained model
torch.save(car_detection_model.state_dict(), 'car_detection_model.pth')
Downloading: "https://download.pytorch.org/models/resnet50-0676ba61.pth" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth 100%|██████████| 97.8M/97.8M [00:00<00:00, 122MB/s]
Streaming output truncated to the last 5000 lines.
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#Refined architecture of Resnet - giving better accuracies
import torchvision.models as models
import torch.optim as optim
import torchvision.transforms as transforms
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
cpu
# to transform input data
data_transforms = {
'/Car Images/Train Images': transforms.Compose([
transforms.Resize((256, 256)), #This transformation resizes the input images to have a height and width of 256 pixels.
transforms.RandomHorizontalFlip(), #This transformation randomly flips the images horizontally with a 50% probability. Horizontal flipping is a common data augmentation technique that helps increase the diversity of the training data.
transforms.RandomRotation(15), #This transformation randomly rotates the images by a maximum of 15 degrees.
transforms.ToTensor(), #This transformation converts the images into PyTorch tensors.
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
]),
'/Car Images/Test Images': transforms.Compose([
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
]),
}
''' In this page (https://pytorch.org/vision/stable/models.html), it says that "All pre-trained models expect input images normalized in the same way,
i.e. mini-batches of 3-channel RGB images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded in to a range of [0, 1]
and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225]"'''
# These values are derived form Imagenet based on mean and standard deviation of millions of images
# These values are commonly used for normalization when working with image data in deep learning, and they come from a widely used dataset called ImageNet
batch_size = 32
image_datasets = {x: torchvision.datasets.ImageFolder(root=data_dir +x, transform=data_transforms[x])
for x in ['/Car Images/Train Images', '/Car Images/Test Images']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True)
for x in ['/Car Images/Train Images', '/Car Images/Test Images']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['/Car Images/Train Images', '/Car Images/Test Images']}
class_names = image_datasets['/Car Images/Train Images'].classes
dataset_sizes
{'/Car Images/Train Images': 8279, '/Car Images/Test Images': 8084}
model_c3 = models.resnet34(pretrained=True)
num_ftrs = model_c3.fc.in_features
model_c3.fc = nn.Linear(num_ftrs, 196)
model_c3 = model_c3.to(device)
model_c3.summary()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model_c3.parameters(), lr=0.01, momentum=0.9)
#This is an optional learning rate scheduler that adjusts the learning rate during training (e.g., learning rate decay).
lrscheduler = optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.1)
Downloading: "https://download.pytorch.org/models/resnet34-b627a593.pth" to /root/.cache/torch/hub/checkpoints/resnet34-b627a593.pth 100%|██████████| 83.3M/83.3M [00:01<00:00, 67.4MB/s]
data_dir + '/Car Images/Train Images'
'/content/drive/MyDrive/MachineLearning/CapstoneProject/Dataset/Car Images/Train Images'
def train_model(model, criterion, optimizer, scheduler, n_epochs = 5):
#Four lists (losses, accuracies, test_accuracies, and test_losses) are initialized to store training and testing metrics over epochs.
losses = []
accuracies = []
test_accuracies = []
test_losses = []
#The model is moved to the specified device, which is presumably either a CPU or GPU.
model.to(device)
model.train()
for epoch in range(n_epochs):
print(f'Epoch {epoch}/{n_epochs-1}')
print('-' * 10)
running_loss = 0.0
running_correct = 0.0
total = 0.0
#For each epoch, it prints the epoch number and enters a loop that iterates over two phases: "train" and "test".
for phase in ['/Car Images/Train Images', '/Car Images/Test Images']:
if phase == '/Car Images/Train Images':
model.train() #In the "train" phase, the model is set to training mode (model.train()), and the training data is used to update the model's parameters.
else:
model.eval() #In the "test" phase, the model is set to evaluation mode (model.eval()), and the testing data is used to evaluate the model's performance without updating its parameters.
running_loss = 0.0
running_corrects = 0
#For each phase (either "train" or "test"), the code loops over the data in batches using the dataloaders dictionary.
for inputs, labels in dataloaders[phase]:
inputs = inputs.to(device)
labels = labels.to(device)
optimizer.zero_grad()
# we perform forward pass, calculate the loss, and compute predictions.
with torch.set_grad_enabled(phase == '/Car Images/Train Images'):
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
#For train phase,we also perform backpropagation and updates the model's parameters using the optimizer.
if phase == '/Car Images/Train Images':
loss.backward()
optimizer.step()
#Running loss and accuracy metrics are computed for each phase.
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
#If the phase is "train," the learning rate scheduler (scheduler) is optionally stepped to adjust the learning rate.
if phase == '/Car Images/Train Images':
scheduler.step()
#At the end of each epoch, the average loss and accuracy for both the "train" and "test" phases are calculated.
epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = running_corrects.double() / dataset_sizes[phase]
#These metrics are appended to the respective lists (losses, accuracies, test_losses, test_accuracies).
if phase == '/Car Images/Train Images':
losses.append(epoch_loss)
accuracies.append(epoch_acc)
else:
test_losses.append(epoch_loss)
test_accuracies.append(epoch_acc)
#The metrics for both phases are printed.
print(f'{phase} Loss: {epoch_loss} Acc: {epoch_acc}')
print()
return model, losses, accuracies, test_losses, test_accuracies
def visual(training_losses, training_accs, test_losses, test_accs, name):
f, ax = plt.subplots(2,2, figsize = (12, 8))
ax[0, 0].plot(training_losses)
ax[0, 0].set_title("Training loss")
ax[0, 1].plot(training_accs)
ax[0, 1].set_title("Training acc")
ax[1, 0].plot(test_losses)
ax[1, 0].set_title("Test loss")
ax[1, 1].plot(test_accs)
ax[1, 1].set_title("Test acc")
f.suptitle(f'Train visualise {name} 100\'s of batches')
plt.show()
print(f'#### Fine-Tuning Model ####')
model_c3, training_losses, training_accs, test_losses, test_accs = train_model(model_c3, criterion, optimizer, lrscheduler, n_epochs=5)
visual(training_losses, training_accs, test_losses, test_accs, 'NN Fine-Tuning Model')
#### Fine-Tuning Model #### Epoch 0/4 ---------- /Car Images/Train Images Loss: 3.823921334111384 Acc: 0.17441720014494505
--------------------------------------------------------------------------- KeyboardInterrupt Traceback (most recent call last) <ipython-input-75-1c6789005c00> in <cell line: 2>() 1 print(f'#### Fine-Tuning Model ####') ----> 2 model_c3, training_losses, training_accs, test_losses, test_accs = train_model(model_c3, criterion, optimizer, lrscheduler, n_epochs=5) 3 visual(training_losses, training_accs, test_losses, test_accs, 'NN Fine-Tuning Model') <ipython-input-73-ff03c892c1a5> in train_model(model, criterion, optimizer, scheduler, n_epochs) 24 running_corrects = 0 25 #For each phase (either "train" or "test"), the code loops over the data in batches using the dataloaders dictionary. ---> 26 for inputs, labels in dataloaders[phase]: 27 inputs = inputs.to(device) 28 labels = labels.to(device) /usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py in __next__(self) 629 # TODO(https://github.com/pytorch/pytorch/issues/76750) 630 self._reset() # type: ignore[call-arg] --> 631 data = self._next_data() 632 self._num_yielded += 1 633 if self._dataset_kind == _DatasetKind.Iterable and \ /usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py in _next_data(self) 673 def _next_data(self): 674 index = self._next_index() # may raise StopIteration --> 675 data = self._dataset_fetcher.fetch(index) # may raise StopIteration 676 if self._pin_memory: 677 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device) /usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index) 49 data = self.dataset.__getitems__(possibly_batched_index) 50 else: ---> 51 data = [self.dataset[idx] for idx in possibly_batched_index] 52 else: 53 data = self.dataset[possibly_batched_index] /usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py in <listcomp>(.0) 49 data = self.dataset.__getitems__(possibly_batched_index) 50 else: ---> 51 data = [self.dataset[idx] for idx in possibly_batched_index] 52 else: 53 data = self.dataset[possibly_batched_index] /usr/local/lib/python3.10/dist-packages/torchvision/datasets/folder.py in __getitem__(self, index) 243 """ 244 path, target = self.samples[index] --> 245 sample = self.loader(path) 246 if self.transform is not None: 247 sample = self.transform(sample) /usr/local/lib/python3.10/dist-packages/torchvision/datasets/folder.py in default_loader(path) 282 return accimage_loader(path) 283 else: --> 284 return pil_loader(path) 285 286 /usr/local/lib/python3.10/dist-packages/torchvision/datasets/folder.py in pil_loader(path) 261 # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835) 262 with open(path, "rb") as f: --> 263 img = Image.open(f) 264 return img.convert("RGB") 265 /usr/local/lib/python3.10/dist-packages/PIL/Image.py in open(fp, mode, formats) 3440 exclusive_fp = True 3441 -> 3442 prefix = fp.read(16) 3443 3444 preinit() KeyboardInterrupt:
# Run 3 - trying with YOLO
!pip install ultralytics
Requirement already satisfied: ultralytics in /usr/local/lib/python3.10/dist-packages (8.2.50) Requirement already satisfied: numpy<2.0.0,>=1.23.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.25.2) Requirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (3.7.1) Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.8.0.76) Requirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (10.4.0) Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (6.0.1) Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.31.0) Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.11.4) Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.3.0+cu121) Requirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.18.0+cu121) Requirement already satisfied: tqdm>=4.64.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.66.4) Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from ultralytics) (5.9.5) Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.10/dist-packages (from ultralytics) (9.0.0) Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.0.3) Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.13.1) Requirement already satisfied: ultralytics-thop>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.0.0) Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from 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satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (12.1.105) Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (2.3.0) Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.8.0->ultralytics) (12.5.82) Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.16.0) Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.8.0->ultralytics) (2.1.5) Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.8.0->ultralytics) (1.3.0)
from ultralytics import YOLO
model = YOLO('yolov8n.pt')
#model.summary()
#import a random image
imgs, labels = next(train_images)
plt.imshow(imgs[1])
<matplotlib.image.AxesImage at 0x7e7e9a0168f0>
img = imgs[1]
#img = cv2.resize(img, None, fx=0.4, fy=0.4)
height, width, channels = img.shape
print(height, width, channels)
224 224 3
#do not understand why nothing is detected with YoLO!
results = model.predict(img)
0: 640x640 (no detections), 390.5ms Speed: 113.6ms preprocess, 390.5ms inference, 8.4ms postprocess per image at shape (1, 3, 640, 640)
# Access the first element of the results list, assuming it contains the result object
result = results[0]
# Now you can access the names attribute
print(result.names)
{0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}
#reference: https://www.freecodecamp.org/news/how-to-detect-objects-in-images-using-yolov8/
#reference: https://www.freecodecamp.org/news/how-to-detect-objects-in-images-using-yolov8/
result = results[0]
print("No. of objects detected", len(result.boxes))
# Check if any boxes were detected before iterating
if result.boxes:
for box in result.boxes:
class_id = result.names[box.cls[0].item()]
cords = box.xyxy[0].tolist()
cords = [round(x) for x in cords]
conf = round(box.conf[0].item(), 2)
print("Object type:", class_id)
print("Coordinates:", cords)
print("Probability:", conf)
print("---")
else:
print("No objects detected in the image.")
No. of objects detected 0 No objects detected in the image.
#Attempt 4 - trying mobilenet model
#reference: https://github.com/prakhargurawa/Vehicle-Detection-Classification-YOLO-MobileNet/blob/main/MobileNet_TransferLearning.py
from keras.applications import MobileNetV2
# Reference : https://analyticsindiamag.com/a-practical-guide-to-implement-transfer-learning-in-tensorflow/
base_model = MobileNetV2(weights='imagenet',include_top=False,input_shape=(192, 192, 3)) # imports the mobilenet model and discards the last 1000 neuron layer.
# Freeze the base model
base_model.trainable = False
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_192_no_top.h5 9406464/9406464 [==============================] - 0s 0us/step
model = Sequential([
base_model,
#Dense(512, activation='relu',kernel_regularizer='l1'),
Dense(512, activation='relu'),
Dropout(0.2),
Dense(512, activation='relu'),
Dropout(0.2),
Dense(196, activation='softmax')
])
# Unfreeze the new layers
for layer in model.layers[0:]:
layer.trainable = True
print("************************ LAYERS ************************")
for layer in model.layers:
print(layer.name,layer.trainable)
print("********************************************************")
print(model.summary())
# Reference : https://www.pyimagesearch.com/2019/07/08/keras-imagedatagenerator-and-data-augmentation/
import keras
import numpy as np
from keras.applications import MobileNetV2
from keras.layers import Dense, Flatten,Dropout
from keras.models import Sequential
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
from keras import regularizers
train_datagen = ImageDataGenerator(preprocessing_function=keras.applications.mobilenet.preprocess_input,validation_split=0.2,
rescale=1./255,
shear_range=0.1,
zoom_range=0.1,
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=True,
vertical_flip=True)
train_generator = train_datagen.flow_from_directory(
data_dir + '/Car Images/Train Images',
target_size=(192,192),
batch_size=64,
class_mode='categorical',
subset='training',
shuffle=True)
validation_generator = train_datagen.flow_from_directory(
data_dir + '/Car Images/Test Images',
target_size=(192, 192),
batch_size=64,
class_mode='categorical',
subset='validation',
shuffle=True)
# Selection of optimizer : https://towardsdatascience.com/7-tips-to-choose-the-best-optimizer-47bb9c1219e
# Adam is the best among the adaptive optimizers in most of the cases.
# Adam optimizer,loss function will be binary cross entropy,evaluation metric will be accuracy
algo = "Adam"
model.compile(optimizer = keras.optimizers.Adam(lr=0.0001), loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
step_size_train=train_generator.n//train_generator.batch_size
step_size_val=validation_generator.n//validation_generator.batch_size
ep = 20
# Check the number of classes in your dataset
# Check the number of classes in your dataset
num_classes = train_generator.num_classes
# Modify the last layer of your model (assuming it's a Sequential model)
model.layers[-1].units = num_classes # Set the number of output units to match the number of classes
model.layers[-1].activation = tf.keras.activations.softmax # Use softmax for categorical classification
# Add a Flatten layer before the final Dense layer if there's an extra dimension
if len(model.layers[-1].input_shape) > 2: # Check if there's an extra dimension (e.g., from convolutional layers)
model.add(tf.keras.layers.Flatten()) # Flatten the output before the final Dense layer
# Recompile the model with the updated output layer
model.compile(optimizer=keras.optimizers.Adam(lr=0.0001),
loss=tf.keras.losses.CategoricalCrossentropy(), # Use CategoricalCrossentropy without from_logits=True for softmax output
metrics=['accuracy'])
# Now try fitting the model again
history = model.fit(x=train_generator,
steps_per_epoch=step_size_train,
validation_data=validation_generator,
validation_steps=step_size_val,
epochs=ep)
#Attempt 1 - too many paramenters 99 mn - takes long to run, hence aborted
# define model
model = Sequential()
model.add(Conv2D(224, (3, 3), padding='same',input_shape=(224,224,3),activation='relu'))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same',activation='relu'))
model.add(Conv2D(64, (3, 3), padding='same',activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(196, activation='softmax'))
model.compile(optimizers.Adam(learning_rate=0.0001),loss="categorical_crossentropy", metrics=["accuracy"])
STEP_SIZE_TRAIN=train_images.n//train_images.batch_size
STEP_SIZE_VALID=test_images.n//test_images.batch_size
model.summary()
# fit model - seems to run with Pillow update fix, but aborated as it was taking way too long
model.fit_generator(generator=train_images,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=test_images,
validation_steps=STEP_SIZE_VALID,
epochs=10)
#not sure if this is the right way of training (alternate to above - anyway - this wasn't runnign and giving graph execution error)
history_n = model.fit(train_images, steps_per_epoch=len(train_images),
validation_data=test_images,
validation_steps=len(test_images), epochs=20)
#Attempt 2 - model with less parameters - 23 mn
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)))
model.add(MaxPooling2D((2, 2)))
#model.add(Dropout(0.5))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Dropout(0.25))
#model.add(Conv2D(64, (3, 3), activation='relu'))
#model.add(MaxPooling2D((2), 2))
model.add(Flatten())
#model.add(BatchNormalization())
model.add(Dense(128, activation='relu'))
#model.add(Dropout(0.5))
model.add(Dense(256, activation='relu'))
#model.add(Dropout(0.25))
model.add(Dense(196, activation='softmax'))
model.summary()
#Build the model with adam optimizer and categorical cross entropy loss function
model.compile(optimizer='adam',
loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
STEP_SIZE_TRAIN=train_images.n//train_images.batch_size
STEP_SIZE_VALID=test_images.n//test_images.batch_size
#yet to try and run this
model.fit_generator(generator=train_images,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=test_images,
validation_steps=STEP_SIZE_VALID,
epochs=10)
# Same error as model A3, gives rise to error after 1 epoch
history_n = model.fit(train_images, steps_per_epoch=len(train_images),
validation_data=test_images,
validation_steps=len(test_images), epochs=10)
# Run 3 - trying with YOLO from Grear learning code
# https://www.mygreatlearning.com/blog/yolo-object-detection-using-opencv/
# Giving some errror - didnt try to debug
# Load Yolo - GL code
print("LOADING YOLO")
net = cv2.dnn.readNet("yolov3.weights", "yolov31.cfg")
#save all the names in file o the list classes
classes = []
with open("coco.names", "r") as f:
classes = [line.strip() for line in f.readlines()]
#get layers of the network
layer_names = net.getLayerNames()
#Determine the output layer names from the YOLO model
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
print("YOLO LOADED")
# Capture frame-by-frame
#img = cv2.imread("test_img.jpg")
img = ims[0]
#img = cv2.resize(img, None, fx=0.4, fy=0.4)
height, width, channels = img.shape
# USing blob function of opencv to preprocess image
blob = cv2.dnn.blobFromImage(img, 1 / 255.0, (416, 416),swapRB=True, crop=False)
#Detecting objects
net.setInput(blob)
outs = net.forward(output_layers)
# Showing informations on the screen
class_ids = []
confidences = []
boxes = []
for out in outs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5:
# Object detected
center_x = int(detection[0] * width)
center_y = int(detection[1] * height)
w = int(detection[2] * width)
h = int(detection[3] * height)
# Rectangle coordinates
x = int(center_x - w / 2)
y = int(center_y - h / 2)
boxes.append([x, y, w, h])
confidences.append(float(confidence))
class_ids.append(class_id)
#We use NMS function in opencv to perform Non-maximum Suppression
#we give it score threshold and nms threshold as arguments.
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
colors = np.random.uniform(0, 255, size=(len(classes), 3))
for i in range(len(boxes)):
if i in indexes:
x, y, w, h = boxes[i]
label = str(classes[class_ids[i]])
color = colors[class_ids[i]]
cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
cv2.putText(img, label, (x, y -5),cv2.FONT_HERSHEY_SIMPLEX,1/2, color, 2)
cv2.imshow("Image",img)
cv2.waitKey(0)
!python -V
import torch
print(torch.__version__)
# Note that some model code requires torch version < 1.9, but python 3.10 does not support torch models of this version
#Attempt 5 - RCNN
!pip install gluoncv
!pip install mxnet
!pip install --upgrade Pillow
!pip install torch>=1.4.0,<2.0.0
import mxnet as mx
import gluoncv
from google.colab.patches import cv2_imshow
from gluoncv import model_zoo, data, utils
net = model_zoo.get_model('faster_rcnn_resnet50_v1b_voc', pretrained=True)
#Attempt 7
#reference https://www.kaggle.com/code/deepbear/pytorch-car-classifier-90-accuracy
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.models as models
import torchvision.transforms as transforms
import time
import os
import PIL.Image as Image
from IPython.display import display
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
train_tfms = transforms.Compose([transforms.Resize((400, 400)),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(15),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
test_tfms = transforms.Compose([transforms.Resize((400, 400)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
dataset = torchvision.datasets.ImageFolder(root=data_dir + data_dir_tmp + '/Car Images/Train Images', transform = train_tfms)
trainloader = torch.utils.data.DataLoader(dataset, batch_size = 32, shuffle=True, num_workers = 2)
dataset2 = torchvision.datasets.ImageFolder(root=data_dir + data_dir_tmp + '/Car Images/Test Images', transform = test_tfms)
testloader = torch.utils.data.DataLoader(dataset2, batch_size = 32, shuffle=False, num_workers = 2)
def train_model(model, criterion, optimizer, scheduler, n_epochs = 5):
losses = []
accuracies = []
test_accuracies = []
# set the model to train mode initially
model.train()
for epoch in range(n_epochs):
since = time.time()
running_loss = 0.0
running_correct = 0.0
for i, data in enumerate(trainloader, 0):
# get the inputs and assign them to cuda
inputs, labels = data
#inputs = inputs.to(device).half() # uncomment for half precision model
inputs = inputs.to(device)
labels = labels.to(device)
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs)
_, predicted = torch.max(outputs.data, 1)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# calculate the loss/acc later
running_loss += loss.item()
running_correct += (labels==predicted).sum().item()
epoch_duration = time.time()-since
epoch_loss = running_loss/len(trainloader)
epoch_acc = 100/32*running_correct/len(trainloader)
print("Epoch %s, duration: %d s, loss: %.4f, acc: %.4f" % (epoch+1, epoch_duration, epoch_loss, epoch_acc))
losses.append(epoch_loss)
accuracies.append(epoch_acc)
# switch the model to eval mode to evaluate on test data
model.eval()
test_acc = eval_model(model)
test_accuracies.append(test_acc)
# re-set the model to train mode after validating
model.train()
scheduler.step(test_acc)
since = time.time()
print('Finished Training')
return model, losses, accuracies, test_accuracies
def eval_model(model):
correct = 0.0
total = 0.0
with torch.no_grad():
for i, data in enumerate(testloader, 0):
images, labels = data
#images = images.to(device).half() # uncomment for half precision model
images = images.to(device)
labels = labels.to(device)
outputs = model_ft(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
test_acc = 100.0 * correct / total
print('Accuracy of the network on the test images: %d %%' % (test_acc))
return test_acc
model_ft = models.resnet34(pretrained=True)
num_ftrs = model_ft.fc.in_features
# replace the last fc layer with an untrained one (requires grad by default)
model_ft.fc = nn.Linear(num_ftrs, 196)
model_ft = model_ft.to(device)
# uncomment this block for half precision model
"""
model_ft = model_ft.half()
for layer in model_ft.modules():
if isinstance(layer, nn.BatchNorm2d):
layer.float()
"""
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model_ft.parameters(), lr=0.01, momentum=0.9)
"""
probably not the best metric to track, but we are tracking the training accuracy and measuring whether
it increases by atleast 0.9 per epoch and if it hasn't increased by 0.9 reduce the lr by 0.1x.
However in this model it did not benefit me.
"""
lrscheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', patience=3, threshold = 0.9)
model_ft, training_losses, training_accs, test_accs = train_model(model_ft, criterion, optimizer, lrscheduler, n_epochs=10)
f, axarr = plt.subplots(2,2, figsize = (12, 8))
axarr[0, 0].plot(training_losses)
axarr[0, 0].set_title("Training loss")
axarr[0, 1].plot(training_accs)
axarr[0, 1].set_title("Training acc")
axarr[1, 0].plot(test_accs)
axarr[1, 0].set_title("Test acc")
def find_classes(dir):
classes = os.listdir(dir)
classes.sort()
class_to_idx = {classes[i]: i for i in range(len(classes))}
return classes, class_to_idx
classes, c_to_idx = find_classes(dataset_dir+"train")
# switch the model to evaluation mode to make dropout and batch norm work in eval mode
model_ft.eval()
# transforms for the input image
loader = transforms.Compose([transforms.Resize((400, 400)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
image = Image.open(dataset_dir+"test/Mercedes-Benz C-Class Sedan 2012/01977.jpg")
image = loader(image).float()
image = torch.autograd.Variable(image, requires_grad=True)
image = image.unsqueeze(0)
image = image.cuda()
output = model_ft(image)
conf, predicted = torch.max(output.data, 1)
# get the class name of the prediction
display(Image.open(dataset_dir+"test/Mercedes-Benz C-Class Sedan 2012/01977.jpg"))
print(classes[predicted.item()], "confidence: ", conf.item())
def transform_classes(args):
"""
data/0/ -> 0
data/1/ -> 1
data/2/ -> 1
"""
new_classes = np.array([0,1,1])
x,y = args
y = np.argmax(y, axis=1) # One hot to integer
y = new_classes[y] # Changing the target
y = np.eye(2)[y] # Going back to one hot
return x,y
train_generator = map(transform_classes, train_generator)
# Creating train & test from their DataFrames
x_train = mapped_train['Image']
y_train = mapped_train['Class']
x_test = mapped_test['Image']
y_test = mapped_test['Class']
# Resizing the images, converting the grey ones to color and storing them in training array
training = []
for i in x_train:
color_img = i
try:
color_img = cv2.cvtColor(color_img, cv2.COLOR_RGB2GRAY)
except:
training.append(cv2.resize(color_img,(200,200)))
continue
training.append(cv2.resize(color_img,(200,200)))
training = np.asarray(training)
training.shape
plt.imshow(training[10])
# Resizing the images, converting the grey ones to color and storing them in testing array
testing = []
for i in x_test:
color_img = i
try:
color_img = cv2.cvtColor(color_img, cv2.COLOR_RGB2GRAY)
except:
testing.append(cv2.resize(color_img,(200,200)))
continue
testing.append(cv2.resize(color_img,(200,200)))
testing = np.asarray(testing)
testing.shape
plt.imshow(testing[10])
# Sharpening and normalizing the train images
kernel = np.array([[0, -1, 0],
[-1, 5, -1],
[0, -1, 0]])
new_train = []
for i in training:
sharpened = cv2.filter2D(i, -1, kernel)
img_normalized = cv2.normalize(sharpened, None, 0, 255, cv2.NORM_MINMAX)
new_train.append(img_normalized)
new_train = np.asarray(new_train)
new_train.shape
plt.imshow(new_train[10])
# Sharpening and normalizing the test images
new_test = []
for i in testing:
sharpened = cv2.filter2D(i, -1, kernel)
img_normalized = cv2.normalize(sharpened, None, 0, 255, cv2.NORM_MINMAX)
new_test.append(img_normalized)
new_test = np.asarray(new_test)
new_test.shape
plt.imshow(new_test[10])
classes = np.unique(y_train)
classes
class_labels = []
for c in classes:
val = np.where(merged_df1['Class'].eq(c))[0][0]
class_labels.append(merged_df1.iloc[val]['Label'])
class_labels
from sklearn.preprocessing import LabelBinarizer
lb = LabelBinarizer()
y_train_new = lb.fit_transform(y_train)
#y_train_new = np_utils.to_categorical(y_train)
y_train_new.shape
y_test_new = lb.fit_transform(y_test)
y_test_new.shape
from sklearn.preprocessing import LabelEncoder
from keras.utils import to_categorical
le = LabelEncoder()
train_label = le.fit_transform(y_train)
train_label = to_categorical(train_label)
train_label[0]
test_label = le.fit_transform(y_test)
test_label = to_categorical(test_label)
test_label[0]
import gc
del merged_df1
del testing
del training
del merged_df
del new_df
del df_train_images
del df_test_images
del train_images
del test_images
gc.collect()
new_train = np.expand_dims(new_train, axis=3)
new_test = np.expand_dims(new_test, axis=3)
def to_grayscale_then_rgb(image):
#image = tf.image.rgb_to_grayscale(image)
image = tf.image.grayscale_to_rgb(image)
return image
new_train = new_train/255.0
new_test = new_test/255.0
new_train[0]
generator = ImageDataGenerator(rotation_range = 100,zoom_range = 0.1,width_shift_range = 0.1,
height_shift_range = 0.1,horizontal_flip = True,vertical_flip = True
)
training_set = generator.flow(new_train,y_train,batch_size=32,seed=42,shuffle=True)
testing_set = generator.flow(new_test,y_test,batch_size=32,seed=42,shuffle=True)
# Creating a base CNN model to test the model's performance
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(200, 200, 1)))
model.add(MaxPooling2D((2, 2)))
#model.add(Dropout(0.5))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Dropout(0.25))
#model.add(Conv2D(64, (3, 3), activation='relu'))
#model.add(MaxPooling2D((2), 2))
model.add(Flatten())
#model.add(BatchNormalization())
model.add(Dense(128, activation='relu'))
#model.add(Dropout(0.5))
model.add(Dense(256, activation='relu'))
#model.add(Dropout(0.25))
model.add(Dense(196, activation='softmax'))
model.compile(optimizer='adam',
loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
model.summary()
epochs=20
history = model.fit(new_train,
y_train_new,
validation_data=(new_test,y_test_new),
steps_per_epoch=len(new_train) // 32,
epochs=epochs,
batch_size = 32
)
#Model 2
model2 = Sequential([
tf.keras.layers.Rescaling(1./255, input_shape=(200, 200, 1)),
tf.keras.layers.Conv2D(16, 3, padding='same', activation='relu'),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Conv2D(64, 3, padding='same', activation='relu'),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(196)
])
model2.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
arr = le.fit_transform(y_train)
arr1 = le.fit_transform(y_test)
tr = to_categorical(y_train)
tst = to_categorical(y_test)
len(tr)
epochs=10
history1 = model2.fit(new_train,arr,
validation_data=(new_test,arr1),
#steps_per_epoch=len(new_train) // 32,
epochs=epochs,
batch_size = 32
)
#Run till this point***
img1 = mapped_train['Image'][40]
img1 = np.asarray(img1)
image_test = np.asarray(img1)
plt.imshow(image_test)
kernel = np.array([[0, -1, 0],
[-1, 5, -1],
[0, -1, 0]])
sharpened = cv2.filter2D(image_test, -1, kernel)
plt.imshow(sharpened)
X = mapped_train['X'][40]
Y = mapped_train['Y'][40]
W = mapped_train['Width'][40]
H = mapped_train['Height'][40]
img_g = cv2.GaussianBlur(sharpened,(3,3),0)
plt.imshow(img_g)
# Resizing the image size to half ie., from 128X128 to 64X64
img_resized = cv2.resize(image_test,None,fx=0.50,fy=0.50)
#Applying Gaussian Blur
img_g = cv2.GaussianBlur(img_resized,(3,3),0)
#Displaying preprocessed and original images
print("Resized to 50% and applied Gaussian Blurring with kernel size 3X3")
plt.imshow(img_g)
mask = np.zeros(sharpened.shape[:2],np.uint8)
bgdModel = np.zeros((1,65),np.float64)
fgdModel = np.zeros((1,65),np.float64)
rect = (X,Y,W,H)
cv2.grabCut(sharpened,mask,rect,bgdModel,fgdModel,5,cv2.GC_INIT_WITH_RECT)
mask2 = np.where((mask==2)|(mask==0),0,1).astype('uint8')
image_test1 = sharpened*mask2[:,:,np.newaxis]
plt.imshow(image_test1),plt.colorbar(),plt.show()
mask[mask2 == 0] = 0
mask[mask2 == 255] = 1
mask = np.zeros(image_test1.shape[:2],np.uint8)
mask, bgdModel, fgdModel = cv2.grabCut(image_test1,mask,None,bgdModel,fgdModel,5,cv2.GC_INIT_WITH_MASK)
mask = np.where((mask==2)|(mask==0),0,1).astype('uint8')
image_test2 = image_test1*mask[:,:,np.newaxis]
plt.imshow(image_test2),plt.colorbar(),plt.show()
plt.imshow(image_test1)
grey = cv2.cvtColor(img_g, cv2.COLOR_BGR2GRAY)
plt.imshow(grey)
# perform image thresholding
ret, thresh = cv2.threshold(grey, 20, 255, cv2.THRESH_BINARY)
# plot image after thresholding
plt.imshow(thresh, cmap = 'gray')
# apply image dilation
kernel = np.ones((3,3),np.uint8)
dilated = cv2.dilate(thresh,kernel,iterations = 1)
# plot dilated image
plt.imshow(dilated, cmap = 'gray')
fast = cv2.fastNlMeansDenoisingColored(image_test, None, 10, 10, 7, 15)
plt.imshow(fast)
norm_img = np.zeros((image_test.shape[0], image_test.shape[1]))
image_test = cv2.normalize(image_test, norm_img, 0, 255, cv2.NORM_MINMAX)
plt.imshow(image_test)
def deskew(image):
co_ords = np.column_stack(np.where(image > 0))
angle = cv2.minAreaRect(co_ords)[-1]
if angle < -45:
angle = -(90 + angle)
else:
angle = -angle
(h, w) = image.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, angle, 1.0)
rotated = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC,borderMode=cv2.BORDER_REPLICATE)
return rotated
# Convert the frame to grayscale for edge detection
gray = cv2.cvtColor(image_test, cv2.COLOR_BGR2GRAY)
# Apply Gaussian blur to reduce noise and smoothen edges
blurred = cv2.GaussianBlur(src=gray, ksize=(3, 5), sigmaX=0.5)
# Perform Canny edge detection
edges = cv2.Canny(blurred, 20, 200)
plt.imshow(edges)
contours, hierarchy = cv2.findContours(edges,
cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
imgn = cv2.drawContours(sharpened, contours, -1, (0, 255, 0), 3)
plt.imshow(imgn)
mobilenet_v2 = "https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4"
class DeepLabModel(object):
"""Class to load deeplab model and run inference."""
INPUT_TENSOR_NAME = 'ImageTensor:0'
OUTPUT_TENSOR_NAME = 'SemanticPredictions:0'
# INPUT_SIZE = 1024
INPUT_SIZE = 513
FROZEN_GRAPH_NAME = 'frozen_inference_graph'
def __init__(self, tarball_path):
"""Creates and loads pretrained deeplab model."""
self.graph = tf.Graph()
graph_def = None
# Extract frozen graph from tar archive.
tar_file = tarfile.open(tarball_path)
for tar_info in tar_file.getmembers():
if self.FROZEN_GRAPH_NAME in os.path.basename(tar_info.name):
file_handle = tar_file.extractfile(tar_info)
graph_def = tf.compat.v1.GraphDef.FromString(file_handle.read())
break
tar_file.close()
if graph_def is None:
raise RuntimeError('Cannot find inference graph in tar archive.')
with self.graph.as_default():
tf.import_graph_def(graph_def, name='')
self.sess = tf.compat.v1.Session(graph=self.graph)
def run(self, image):
"""Runs inference on a single image.
Args:
image: A PIL.Image object, raw input image.
Returns:
resized_image: RGB image resized from original input image.
seg_map: Segmentation map of `resized_image`.
"""
width = image.shape[0]
height = image.shape[1]
resize_ratio = 1.0 * self.INPUT_SIZE / max(width, height)
print(width, height)
print("Resize Ratio - {}".format(resize_ratio))
target_size = (int(resize_ratio * width), int(resize_ratio * height))
print(target_size)
# target_size = (width, height)
resized_image = Image.fromarray(np.array(image, dtype=np.float32)).convert('RGB').resize(target_size, Image.ANTIALIAS)
batch_seg_map = self.sess.run(
self.OUTPUT_TENSOR_NAME,
feed_dict={self.INPUT_TENSOR_NAME: [np.asarray(resized_image)]})
seg_map = batch_seg_map[0]
return resized_image, seg_map
def create_pascal_label_colormap():
"""Creates a label colormap used in PASCAL VOC segmentation benchmark.
Returns:
A Colormap for visualizing segmentation results.
"""
colormap = np.zeros((256, 3), dtype=int)
ind = np.arange(256, dtype=int)
for shift in reversed(range(8)):
for channel in range(3):
colormap[:, channel] |= ((ind >> channel) & 1) << shift
ind >>= 3
return colormap
def label_to_color_image(label):
"""Adds color defined by the dataset colormap to the label.
Args:
label: A 2D array with integer type, storing the segmentation label.
Returns:
result: A 2D array with floating type. The element of the array
is the color indexed by the corresponding element in the input label
to the PASCAL color map.
Raises:
ValueError: If label is not of rank 2 or its value is larger than color
map maximum entry.
"""
if label.ndim != 2:
raise ValueError('Expect 2-D input label')
colormap = create_pascal_label_colormap()
if np.max(label) >= len(colormap):
raise ValueError('label value too large.')
return colormap[label]
def vis_segmentation(image, seg_map):
"""Visualizes input image, segmentation map and overlay view."""
plt.figure(figsize=(15, 5))
grid_spec = gridspec.GridSpec(1, 4, width_ratios=[6, 6, 6, 1])
plt.subplot(grid_spec[0])
plt.imshow(image)
plt.axis('off')
plt.title('input image')
plt.subplot(grid_spec[1])
seg_image = label_to_color_image(seg_map).astype(np.uint8)
plt.imshow(seg_image)
plt.axis('off')
plt.title('segmentation map')
plt.subplot(grid_spec[2])
plt.imshow(image)
plt.imshow(seg_image, alpha=0.7)
plt.axis('off')
plt.title('segmentation overlay')
unique_labels = np.unique(seg_map)
ax = plt.subplot(grid_spec[3])
plt.imshow(
FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation='nearest')
ax.yaxis.tick_right()
plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels])
plt.xticks([], [])
ax.tick_params(width=0.0)
plt.grid('off')
plt.show()
LABEL_NAMES = np.asarray([
'background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus',
'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tv'
])
FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)
FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP)
import tarfile
import tempfile
import tensorflow as tf
from six.moves import urllib
MODEL_NAME = 'mobilenetv2_coco_voctrainaug' # @param ['mobilenetv2_coco_voctrainaug', 'mobilenetv2_coco_voctrainval', 'xception_coco_voctrainaug', 'xception_coco_voctrainval']_DOWNLOAD_URL_PREFIX = 'http://download.tensorflow.org/models/'
_MODEL_URLS = {
'mobilenetv2_coco_voctrainaug':
'deeplabv3_mnv2_pascal_train_aug_2018_01_29.tar.gz',
'mobilenetv2_coco_voctrainval':
'deeplabv3_mnv2_pascal_trainval_2018_01_29.tar.gz',
'xception_coco_voctrainaug':
'deeplabv3_pascal_train_aug_2018_01_04.tar.gz',
'xception_coco_voctrainval':
'deeplabv3_pascal_trainval_2018_01_04.tar.gz',
}
_TARBALL_NAME = 'deeplab_model.tar.gz'
model_dir = tempfile.mkdtemp()
tf.io.gfile.makedirs(model_dir)
download_path = os.path.join(model_dir, _TARBALL_NAME)
print('downloading model, this might take a while...')
urllib.request.urlretrieve('http://download.tensorflow.org/models/' + _MODEL_URLS[MODEL_NAME],
download_path)
print('download completed! loading DeepLab model...')
MODEL = DeepLabModel(download_path)
print('model loaded successfully!')
#global resized_im, seg_map
def run_visualization(image):
try:
#original_im = Image.open(IMAGE_NAME)
original_im = image
except IOError:
print('Cannot retrieve image. Please check url: ' + url)
returnprint('running deeplab on image')
resized_im, seg_map = MODEL.run(original_im)
vis_segmentation(resized_im, seg_map)
return resized_im, seg_map
resized_im, seg_map = MODEL.run(grey)
import matplotlib.gridspec as gridspec
vis_segmentation(resized_im, seg_map)
print(type(resized_im))
numpy_image = np.array(resized_im)
from copy import deepcopy
person_not_person_mapping = deepcopy(numpy_image)
person_not_person_mapping[seg_map != 7] = 0
person_not_person_mapping[seg_map == 7] = 255
plt.imshow(person_not_person_mapping)
orig_imginal = np.array(sharpened)
mapping_resized = cv2.resize(person_not_person_mapping,
(sharpened.shape[1],
sharpened.shape[0]),
Image.ANTIALIAS)
gray = cv2.cvtColor(mapping_resized, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray,(15,15),0)
ret3,thresholded_img = cv2.threshold(blurred,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
plt.imshow(thresholded_img)
# Perform Canny edge detection
canny = cv2.Canny(person_not_person_mapping, 20, 200)
plt.imshow(canny)
sharpened_image = cv2.Laplacian(sharpened, cv2.CV_64F)
plt.imshow(sharpened_image)